1
|
Bao Y, Tian H, Wang X. Effects of climate change and ozone on vegetation phenology on the Tibetan Plateau. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 932:172780. [PMID: 38685428 DOI: 10.1016/j.scitotenv.2024.172780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 05/02/2024]
Abstract
The vegetation phenology, encompassing the start (SOS) and end (EOS) of the growing season on the Tibetan Plateau, has been significantly impacted by global climate change. Furthermore, ozone (O3) has gradually become the main pollutant in this region, substantially influencing carbon cycle and ecosystems on Earth. While ongoing studies have focused mainly on the implications of climate parameters, including temperature, precipitation, and radiation, the effects of O3 on the SOS and EOS remain unclear. Here, we compared the responses and sensitivities of the SOS and EOS to both climatic factors and O3 in this region. With the use of partial correlation analysis, we found that increased precipitation was the most important factor influencing the SOS and caused earlier occurrence (4.8 % vs. 21.9 %) for most plant functional types. In comparison, temperature only dominated in shrublands. In particular, we found that the EOS responded comparably to climatic factors with similar proportions between advancing and delaying patterns. However, higher O3 levels consistently advanced the EOS for almost all plant functional types and was the main factor controlling EOS variations based on the sensitivity analysis. Our results emphasized that O3 pollution should be considered for obtaining better phenological forecasts and determining the impacts of the environment and atmospheric composition on carbon sequestration in terrestrial ecosystems.
Collapse
Affiliation(s)
- Yanlei Bao
- School of Geographical Sciences, Hebei Normal University, Shijiazhuang 050024, China; Department of Hydraulic Engineering, Hebei University of Water Resources and Electric Engineering, Cangzhou 061001, China
| | - Haifeng Tian
- College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
| | - Xiaoyue Wang
- The Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
| |
Collapse
|
2
|
Zhang W, Liu D, Tian H, Pan N, Yang R, Tang W, Yang J, Lu F, Dayananda B, Mei H, Wang S, Shi H. Parsimonious estimation of hourly surface ozone concentration across China during 2015-2020. Sci Data 2024; 11:492. [PMID: 38744849 PMCID: PMC11094007 DOI: 10.1038/s41597-024-03302-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 04/24/2024] [Indexed: 05/16/2024] Open
Abstract
Surface ozone is an important air pollutant detrimental to human health and vegetation productivity, particularly in China. However, high resolution surface ozone concentration data is still lacking, largely hindering accurate assessment of associated environmental impacts. Here, we collected hourly ground ozone observations (over 6 million records), remote sensing products, meteorological data, and social-economic information, and applied recurrent neural networks to map hourly surface ozone data (HrSOD) at a 0.1° × 0.1° resolution across China during 2015-2020. The coefficient of determination (R2) values in sample-based, site-based, and by-year cross-validations were 0.72, 0.65 and 0.71, respectively, with the root mean square error (RMSE) values being 11.71 ppb (mean = 30.89 ppb), 12.81 ppb (mean = 30.96 ppb) and 11.14 ppb (mean = 31.26 ppb). Moreover, it exhibits high spatiotemporal consistency with ground-level observations at different time scales (diurnal, seasonal, annual), and at various spatial levels (individual sites and regional scales). Meanwhile, the HrSOD provides critical information for fine-resolution assessment of surface ozone impacts on environmental and human benefits.
Collapse
Affiliation(s)
- Wenxiu Zhang
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Di Liu
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hanqin Tian
- Schiller Institute of Integrated Science and Society, Boston College, Chestnut Hill, MA, 02467, USA
| | - Naiqin Pan
- Schiller Institute of Integrated Science and Society, Boston College, Chestnut Hill, MA, 02467, USA
- College of Forestry, Wildlife and Environment, Auburn University, Auburn, AL, 36849, USA
| | - Ruqi Yang
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Wenhan Tang
- Department of Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Jia Yang
- Natural Resource Ecology & Management, Oklahoma State University, Stillwater, OK, 74078, USA
| | - Fei Lu
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Buddhi Dayananda
- School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Han Mei
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, 999077, China
| | - Siyuan Wang
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hao Shi
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| |
Collapse
|
3
|
Zhang X, Sun J, Lin W, Xu W, Zhang G, Wu Y, Dai X, Zhao J, Yu D, Xu X. Long-term variations in surface ozone at the Longfengshan Regional Atmosphere Background Station in Northeast China and related influencing factors. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 348:123748. [PMID: 38460592 DOI: 10.1016/j.envpol.2024.123748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/25/2024] [Accepted: 03/06/2024] [Indexed: 03/11/2024]
Abstract
Surface ozone (O3) is a crucial air pollutant that affects air quality, human health, agricultural production, and climate change. Studies on long-term O3 variations and their influencing factors are essential for understanding O3 pollution and its impact. Here, we conducted an analysis of long-term variations in O3 during 2006-2022 at the Longfengshan Regional Atmosphere Background Station (LFS; 44.44°N, 127.36°E, 330.5 m a.s.l.) situated on the northeastern edge of the Northeast China Plains. The maximum daily 8-h average (MDA8) O3 fluctuated substantially, with the annual MDA8 decreasing significantly during 2006-2015 (-0.62 ppb yr-1, p < 0.05), jumping during 2015-2016 and increasing clearly during 2020-2022. Step multiple linear regression models for MDA8 were obtained using meteorological variables, to decompose anthropogenic and meteorological contributions to O3 variations. Anthropogenic activities acted as the primary drivers of the long-term trends of MDA8 O3, contributing 73% of annual MDA8 O3 variability, whereas meteorology played less important roles (27%). Elevated O3 at LFS were primarily associated with airflows originating from the North China Plain, Northeast China Plain, and coastal areas of North China, primarily occurring during the warm months (May-October). Based on satellite products of NO2 and HCHO columns, the O3 photochemical regimes over LFS revealed NOx-limited throughout the period. NO2 increased first, reaching peak in 2011, followed by substantial decrease; while HCHO exhibited significant increase, contributing to decreasing trend in MDA8 O3 during 2006-2015. The plateauing NO2 and decreasing HCHO may contribute to the increase in MDA8 O3 in 2016. Subsequently, both NO2 and HCHO exhibited notable fluctuations, leading to significant changes in O3. The study results fill the gap in the understanding of long-term O3 trends in high-latitude areas in the Northeast China Plain and offer valuable insights for assessing the impact of O3 on crop yields, forest productivity, and climate change.
Collapse
Affiliation(s)
- Xiaoyi Zhang
- State Key Laboratory of Severe Weather, Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing, 100081, China; Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai, 200438, China
| | - Jingmin Sun
- Longfengshan Regional Atmosphere Background Station, China Meteorological Administration (CMA), Heilongjiang, 150200, China
| | - Weili Lin
- Key Laboratory of Ecology and Environment in Minority Areas (Minzu University of China), National Ethnic Affairs Commission, Beijing, 100081, China
| | - Wanyun Xu
- State Key Laboratory of Severe Weather, Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
| | - Gen Zhang
- State Key Laboratory of Severe Weather, Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
| | - Yanling Wu
- Longfengshan Regional Atmosphere Background Station, China Meteorological Administration (CMA), Heilongjiang, 150200, China
| | - Xin Dai
- Longfengshan Regional Atmosphere Background Station, China Meteorological Administration (CMA), Heilongjiang, 150200, China
| | - Jinrong Zhao
- Longfengshan Regional Atmosphere Background Station, China Meteorological Administration (CMA), Heilongjiang, 150200, China
| | - Dajiang Yu
- Longfengshan Regional Atmosphere Background Station, China Meteorological Administration (CMA), Heilongjiang, 150200, China.
| | - Xiaobin Xu
- State Key Laboratory of Severe Weather, Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
| |
Collapse
|
4
|
Yan X, Guo Y, Zhang Y, Chen J, Jiang Y, Zuo C, Zhao W, Shi W. Combining physical mechanisms and deep learning models for hourly surface ozone retrieval in China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 351:119942. [PMID: 38150930 DOI: 10.1016/j.jenvman.2023.119942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/29/2023] [Accepted: 12/23/2023] [Indexed: 12/29/2023]
Abstract
As surface ozone (O3) gains increasing attention, there is an urgent need for high temporal resolution and accurate O3 monitoring. By taking advantage of the progress in artificial intelligence, deep learning models have been applied to satellite based O3 retrieval. However, the underlying physical mechanisms that influence surface O3 into model construction have rarely been considered. To overcome this issue, we considered the physical mechanisms influencing surface O3 and used them to select relevant variable features for developing a novel deep learning model. We used a wide and deep model architecture to account for linear and non-linear relationships between the variables and surface O3. Using the developed model, we performed hourly inversions of surface O3 retrieval over China from 2017 to 2019 (9:00-17:00, local time). The validation results based on sample-based (site-based) methods yielded an R2 of 0.94 (0.86) and an RMSE of 12.79 (19.13) μg/m3, indicating the accuracy of the models. The average surface O3 concentrations in China in 2017, 2018, and 2019 were 82, 78, and 87 μg/m3, respectively. There was a diurnal pattern in surface O3 in China, with levels rising significantly from 55 μg/m3 at 9:00 a.m. to 96 μg/m3 at 15:00. Between 15:00 and 16:00, the O3 concentration remained stable at 95 μg/m3 and decreased slightly thereafter (16:00-17:00). The results of this study contribute to a deeper understanding of the physical mechanisms of ozone and facilitate further studies on ozone monitoring, thereby enhancing our understanding of the spatiotemporal characteristics of ozone.
Collapse
Affiliation(s)
- Xing Yan
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
| | - Yushan Guo
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Yue Zhang
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Jiayi Chen
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Yize Jiang
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Chen Zuo
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Wenji Zhao
- College of Resource Environment and Tourism, Capital Normal University, Beijing, 100048, China
| | - Wenzhong Shi
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| |
Collapse
|
5
|
Liu K, Tan J, Xiao L, Pan RT, Yao XY, Shi FY, Li SZ, Li LH. Spatio-temporal disparities of Clonorchis sinensis infection in animal hosts in China: a systematic review and meta-analysis. Infect Dis Poverty 2023; 12:97. [PMID: 37845775 PMCID: PMC10580589 DOI: 10.1186/s40249-023-01146-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 10/01/2023] [Indexed: 10/18/2023] Open
Abstract
BACKGROUND Clonorchis sinensis, one of the most important food-borne zoonotic trematodes, remains prevalent in China. Understanding its infection status in animals is crucial for controlling human clonorchiasis. Here we conducted a systematic review and meta-analysis to focus on the spatio-temporal disparities of C. sinensis infection in animals in China. METHODS Data on C. sinensis prevalence in snails, the second intermediate hosts, or animal reservoirs in China were extracted from electronic databases including PubMed, Embase, Web of Science, Chinese Wanfang database, CNKI, VIP, and China Biomedical Literature database. A random-effects meta-analysis model was utilized to estimate the pooled prevalence in each of the above animal hosts. Subgroup analysis and multivariable meta-regression were performed to explore potential sources of heterogeneity across studies and compare the temporal disparity of infection rates between high and low epidemic areas. Scatter plots were used to depict the biogeographical characteristics of regions reporting C. sinensis infection in animals. RESULTS The overall pooled prevalence of C. sinensis was 0.9% (95% CI: 0.6-1.2%) in snails, 14.2% (12.7-15.7%) in the second intermediate host, and 14.3% (11.4-17.6%) in animal reservoirs. Prevalence in low epidemic areas (with human prevalence < 1%) decreased from 0.6% (0.2-1.2%) before 1990 to 0.0% (0.0-3.6%) after 2010 in snails (P = 0.0499), from 20.3% (15.6-25.3%) to 8.8% (5.6-12.6%) in the second intermediate hosts (P = 0.0002), and from 18.3% (12.7-24.7%) to 4.7% (1.0-10.4%) in animal reservoirs. However, no similar decrease in prevalence was observed in high epidemic areas (with human prevalence ≥ 1.0%). C. sinensis infections were predominantly reported in areas with altitudes below 2346 m and annual cumulative precipitation above 345 mm and were mostly concentrated in eastern China. CONCLUSIONS There are spatio-temporal disparities in the animal infections of C. sinensis in different areas of China. Animal infections are primarily concentrated in regions with low altitude and high precipitation. The results suggest that implementing One Health-based comprehensive measures targeting both humans and animals, especially in high epidemic areas, is essential for successful eradication of C. sinensis in China.
Collapse
Affiliation(s)
- Kai Liu
- School of Public Health, Weifang Medical University, Weifang, 261053, China
| | - Jing Tan
- School of Public Health, Weifang Medical University, Weifang, 261053, China
| | - Lu Xiao
- School of Public Health, Weifang Medical University, Weifang, 261053, China
| | - Rui-Tai Pan
- School of Public Health, Weifang Medical University, Weifang, 261053, China
| | - Xiao-Yan Yao
- School of Public Health, Weifang Medical University, Weifang, 261053, China
| | - Fu-Yan Shi
- School of Public Health, Weifang Medical University, Weifang, 261053, China
| | - Shi-Zhu Li
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, 200025, China.
- Key Laboratory of Parasite and Vector Biology, Ministry of Health, WHO Collaborating Center for Malaria, Schistosomiasis and Filariasis, Shanghai, 200025, China.
| | - Lan-Hua Li
- School of Public Health, Weifang Medical University, Weifang, 261053, China.
| |
Collapse
|
6
|
Hussain N, Ahmad M, Sipra H, Ali S, Syed JH, Hussain K, Hassan SW. First insight into seasonal variability of urban air quality of northern Pakistan: An emerging issue associated with health risks in Karakoram-Hindukush-Himalaya region. CHEMOSPHERE 2023; 316:137878. [PMID: 36646179 DOI: 10.1016/j.chemosphere.2023.137878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/30/2022] [Accepted: 01/13/2023] [Indexed: 06/17/2023]
Abstract
There is a dire need of air quality monitoring in the high-mountain areas of Karakoram-Hindu Kush-Himalaya (HKH) region, particularly related to the recent activities undergoing the China-Pakistan Economic Corridor (CPEC). This study presents the first baseline monitoring and evaluation findings from Gilgit city, Gilgit-Baltistan. Hourly data collection for air quality parameters (PM2.5, NO, NO2, SO2, O3 and CO) were measured using air-pointer (recordum, Austria) from 1 Jan 2018 to 31 Mar 2018 (winter) and 1 Jun 2018 to 31 Aug 2018 (summer). Our findings depict PM2.5 health limits were crossed in the winter season, while NO, NO2 and SO2 remained below their health limits. O3 and CO showed a rising trend in summer months, crossing the 8-h health limits during the season. Seasonal correlation in meteorology found an inverse relationship between most parameters and temperatures; reverse was true for O3 and CO. In parallel, thermal optical carbon analysis filter-based sampling characterized air quality into mass concentrations of PM2.5, organic carbon (OC), elemental carbon (EC) and various heavy metals. Filter-based PM2.5 correlated well with analyzer-based PM2.5 for all months that were studied, except February and March 2018. PM2.5, OC and EC were higher in summer as compared to winter, whereas higher heavy metal contributions were measured predominantly during summer. Health impacts were found to be above health limits for Ni in children only. Furthermore, principal component analysis-multiple linear regression (PCA-MLR) technique was applied to determine source apportionment, confirming the role of biomass burning in winters, and vehicular emissions in summers, highlighting the need for flexible monitoring of technologies/approaches, and communications among the various public, private agencies, and all relevant stakeholders.
Collapse
Affiliation(s)
- Nasir Hussain
- Department of Environmental Sciences, Karakorum International University, Gilgit, Pakistan; Gilgit-Baltistan Environmental Protection Agency (GB-EPA), Gilgit, Pakistan
| | - Masroor Ahmad
- Department of Environmental Sciences, Karakorum International University, Gilgit, Pakistan
| | - Hassaan Sipra
- Centre for Climate Research and Development, COMSATS University Islamabad, Park Road Tarlai Kalan, 45550, Islamabad, Pakistan
| | - Shuakat Ali
- Department of Environmental Sciences, Karakorum International University, Gilgit, Pakistan
| | - Jabir Hussain Syed
- Department of Meteorology, COMSATS University Islamabad, Park Road Tarlai Kalan, 45550, Islamabad, Pakistan; Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong.
| | - Khadim Hussain
- Gilgit-Baltistan Environmental Protection Agency (GB-EPA), Gilgit, Pakistan
| | - Syed Waqar Hassan
- Gilgit-Baltistan Environmental Protection Agency (GB-EPA), Gilgit, Pakistan
| |
Collapse
|
7
|
Wang L, Zhang J, Wei J, Zong J, Lu C, Du Y, Wang Q. Association of ambient air pollution exposure and its variability with subjective sleep quality in China: A multilevel modeling analysis. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 312:120020. [PMID: 36028077 DOI: 10.1016/j.envpol.2022.120020] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 08/14/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
Growing epidemiological evidence has shown that exposure to ambient air pollution contributes to poor sleep quality. However, whether variability in air pollution exposure affects sleep quality remains unclear. Based on a large sample in China, this study linked individual air pollutant exposure levels and temporal variability with subjective sleep quality. Town-level data on daily air pollution concentration for 30 days prior to the survey date were collected, and the monthly mean value, standard deviations, number of heavily polluted days, and trajectory for six common pollutants were calculated to measure air pollution exposure and its variations. Sleep quality was subjectively assessed using the Pittsburgh Sleep Quality Index (PSQI), and a PSQI score above 5 indicated overall poor sleep quality. Multilevel and negative control models were used. Both air pollution exposure and variability contributed to poor sleep quality. A one-point increase in the one-month mean concentration of particulate matter with aerodynamic diameters of ≤2.5 μm (PM2.5) and ≤10 μm (PM10) led to 0.4% (95% confidence interval (CI): 1.002-1.006) and 0.3% (95% CI: 1.001-1.004) increases in the likelihoods of overall poor sleep quality (PSQI score >5), respectively; the odds ratios of a heavy pollution day with PM2.5 and PM10 were 2.2% (95% CI: 1.012-1.032) and 2.2% (95% CI: 1.012-1.032), respectively. Although the mean concentrations of nitrogen dioxide, sulfur dioxide, and carbon monoxide met the national standard, they contributed to the likelihood of overall poor sleep quality (PSQI score >5). A trajectory of air pollution exposure with maximum variability was associated with a higher likelihood of overall poor sleep quality (PSQI score >5). Subjective measures of sleep latency, duration, and efficiency (derived from PSQI) were affected in most cases. Thus, sleep health improvements should account for air pollution exposure and its variations in China under relatively high air pollution levels.
Collapse
Affiliation(s)
- Lingli Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; National Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jingxuan Zhang
- Shandong Provincial Mental Health Center, Jinan City, Shandong, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, USA
| | - Jingru Zong
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; National Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Chunyu Lu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; National Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yajie Du
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; National Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qing Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; National Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China.
| |
Collapse
|
8
|
Guo W, Yang Y, Chen Q, Zhu Y, Zhang Y, Zhang Y, Liu Y, Li G, Sun W, She J. Chemical reactivity of volatile organic compounds and their effects on ozone formation in a petrochemical industrial area of Lanzhou, Western China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 839:155901. [PMID: 35569665 DOI: 10.1016/j.scitotenv.2022.155901] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/09/2022] [Accepted: 05/09/2022] [Indexed: 06/15/2023]
Abstract
Measurements of ozone (O3) and its precursors were performed in the summer of 2019 in Lanzhou, a petrochemical industrial city, to better understand the reactivity of volatile organic compounds (VOCs) and their effects on O3 production. During the campaign, the daily maximum 8-hour average (MDA8) O3, NO2, and total VOC (TVOC) concentrations reached 72.2 ± 19.9 ppb, 24.9 ± 10.8 ppb, and 50.8 ± 46.1 ppb, respectively. Alkanes, alkenes, halocarbons, aromatics, and alkynes contributed 45.3%, 24.0%, 16.5%, 10.0%, and 4.2% to TVOCs, respectively. The OH reactivity and relative incremental reactivity (RIR) of VOCs at different times were calculated. The results indicated that alkenes played a predominant role, accounting for an average of 68.5% of the initial VOC reactivity. Compared to other regions, alkenes are relatively more important for O3 formation in the petrochemical industry area of Lanzhou, while aromatics are relatively less important. Generally, O3 formation occurred in a VOC-limited regime in the morning and in a transitional regime in the afternoon. The response surface methodology (RSM) combined with a chemical box model was applied to obtain relationships between O3 and its precursors and determine the most effective way to reduce the O3 concentration. Reduction in the non-alkene concentration slightly affected the O3 concentration. In contrast, the effect of nitrogen oxides (NOx) was closely related to the alkene concentration, and NOx concentration reduction could lead to an increase in the O3 concentration when alkenes were abated to less than 80% of the present concentration. To mitigate O3 pollution near the petrochemical industrial area of Lanzhou, reducing the alkene concentration, especially the C4 alkene concentration (1,3-butadiene, cis-2-butene, and trans-2-butene), was the fastest and most effective control strategy. The results of this study serve as a reference for O3 pollution control in petrochemical industrial areas.
Collapse
Affiliation(s)
- Wenkai Guo
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Yanping Yang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China; Northwest Institute of Eco-environmental Resources, Chinese Academy of Sciences, Lanzhou 730000, China; Gansu Environmental Monitoring Center, Lanzhou 730000, China
| | - Qiang Chen
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China.
| | - Yuhuan Zhu
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Yaru Zhang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Yingnan Zhang
- Environment Research Institute, Shandong University, Jinan 250000, China
| | - Yongle Liu
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Guangyao Li
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Wei Sun
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Jing She
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| |
Collapse
|
9
|
An Ensemble Model-Based Estimation of Nitrogen Dioxide in a Southeastern Coastal Region of China. REMOTE SENSING 2022. [DOI: 10.3390/rs14122807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
NO2 (nitrogen dioxide) is a common pollutant in the atmosphere that can have serious adverse effects on the health of residents. However, the existing satellite and ground observation methods are not enough to effectively monitor the spatiotemporal heterogeneity of near-surface NO2 concentrations, which limits the development of pollutant remediation work and medical health research. Based on TROPOMI-NO2 tropospheric column concentration data, supplemented by meteorological data, atmospheric condition reanalysis data and other geographic parameters, combined with classic machine learning models and deep learning networks, we constructed an ensemble model that achieved a daily average near-surface NO2 of 0.03° exposure. In this article, a meteorological hysteretic effects term and a spatiotemporal term were designed, which considerably improved the performance of the model. Overall, our ensemble model performed better, with a 10-fold CV R2 of 0.89, an RMSE of 5.62 µg/m3, and an MAE of 4.04 µg/m3. The model also had good temporal and spatial generalization capability, with a temporal prediction R2 and a spatial prediction R2 of 0.71 and 0.81, respectively, which can be applied to a wider range of time and space. Finally, we used an ensemble model to estimate the spatiotemporal distribution of NO2 in a coastal region of southeastern China from May 2018 to December 2020. Compared with satellite observations, the model output results showed richer details of the spatiotemporal heterogeneity of NO2 concentrations. Due to the advantages of using multi-source data, this model framework has the potential to output products with a higher spatial resolution and can provide a reference for downscaling work on other pollutants.
Collapse
|
10
|
Qiao DW, Yao J, Zhang JW, Li XL, Mi T, Zeng W. Short-term air quality forecasting model based on hybrid RF-IACA-BPNN algorithm. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:39164-39181. [PMID: 35098458 DOI: 10.1007/s11356-021-18355-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 12/23/2021] [Indexed: 06/14/2023]
Abstract
Despite the apparent improvement in air quality in recent years through a series of effective measures, the concentration of PM2.5 and O3 in Chengdu city remains high. And both the two pollutants can cause serious damage to human health and property; consequently, it is imperative to accurately forecast hourly concentration of PM2.5 and O3 in advance. In this study, an air quality forecasting method based on random forest (RF) method and improved ant colony algorithm coupled with back-propagation neural network (IACA-BPNN) are proposed. RF method was used to screen out highly correlated input variables, and the improved ant colony algorithm (IACA) was adopted to combine with BPNN to improve the convergence performance. Two datasets based on two different kinds of monitoring stations along with meteorological data were applied to verify the performance of this proposed model and compared with another five plain models. The results showed that the RF-IACA-BPNN model has the minimum statistical error of the mean absolute error, root mean square error, and mean absolute percentage error, and the values of R2 consistently outperform other models. Thus, it is concluded that the proposed model is suitable for air quality prediction. It was also detected that the performance of the models for the forecasting of the hourly concentrations of PM2.5 were more acceptable at suburban station than downtown station, while the case is just the opposite for O3, on account of the low variability dataset at suburban station.
Collapse
Affiliation(s)
- De-Wen Qiao
- College of Architecture and Environment, Sichuan University, Chengdu, 610065, China
| | - Jian Yao
- College of Architecture and Environment, Sichuan University, Chengdu, 610065, China.
| | - Ji-Wen Zhang
- College of Architecture and Environment, Sichuan University, Chengdu, 610065, China
| | - Xin-Long Li
- College of Architecture and Environment, Sichuan University, Chengdu, 610065, China
| | - Tan Mi
- College of Architecture and Environment, Sichuan University, Chengdu, 610065, China
| | - Wen Zeng
- Institute for Disaster Management and Reconstruction, Sichuan University-the Hong Kong Polytechnic University, Chengdu, Sichuan, China
| |
Collapse
|
11
|
Mao B, Wang Y, Zhao TH, Zhao Q, San Y, Xiao SS. Response of carbon, nitrogen and phosphorus concentration and stoichiometry of plants and soils during a soybean growth season to O 3 stress and straw return in Northeast China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 822:153573. [PMID: 35122851 DOI: 10.1016/j.scitotenv.2022.153573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 01/27/2022] [Accepted: 01/27/2022] [Indexed: 06/14/2023]
Abstract
Carbon (C), nitrogen (N) and phosphorus (P) concentrations and stoichiometry play important roles in biogeochemical cycles of the ecosystems, yet it is still unclear how the allocations of C, N and P concentrations and stoichiometry among plant organs and soils related to O3 stress and straw return. Here, a pot experiment was conducted in open top chambers to monitor the response of C, N and P concentrations and stoichiometry of leaves, stems, roots and soils during a growing season (branching, flowering and podding stages) of soybean (Glycine max; a species highly sensitive to O3) to background O3 concentration (44.8 ± 5.6 ppb), O3 stress (79.7 ± 5.4 ppb) and straw treatment (no straw return and straw return). O3 stress significantly decreased root biomass. Straw return significantly increased root biomass under O3 stress at branching and flowering stages. Generally, O3 stress and straw return showed significant effects on the C, N and P concentrations of leaves and soils, and stoichiometric ratios of leaves, stems and microbial biomass. The C, N and P concentrations and stoichiometry of leaves, stems, roots and soils in response to O3 stress and straw return at the branching stage were inconsistent with the changes observed at the flowering and podding stages. The P conversion efficiency showed significant relationship with root P concentration under the combined effects of O3 stress and straw return. Altogether, the present study indicated that C, N and P concentrations of soybean might be more important than stoichiometric ratios as a driver of root defence against O3 stress in the case of straw return.
Collapse
Affiliation(s)
- Bing Mao
- Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Nanning Normal University), Ministry of Education, Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation (Nanning Normal University), Nanning 530001, China; College of Agronomy, Shenyang Agricultural University, Shenyang 110866, China
| | - Yan Wang
- College of Agronomy, Shenyang Agricultural University, Shenyang 110866, China
| | - Tian-Hong Zhao
- College of Agronomy, Shenyang Agricultural University, Shenyang 110866, China.
| | - Qiong Zhao
- School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
| | - Yu San
- College of Agronomy, Shenyang Agricultural University, Shenyang 110866, China
| | - Shuang-Shuang Xiao
- Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Nanning Normal University), Ministry of Education, Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation (Nanning Normal University), Nanning 530001, China
| |
Collapse
|
12
|
Wang J, Zhang Y, Wu Z, Luo S, Song W, Wang X. Ozone episodes during and after the 2018 Chinese National Day holidays in Guangzhou: Implications for the control of precursor VOCs. J Environ Sci (China) 2022; 114:322-333. [PMID: 35459495 DOI: 10.1016/j.jes.2021.09.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 08/29/2021] [Accepted: 09/06/2021] [Indexed: 06/14/2023]
Abstract
The impact of reducing industrial emissions of volatile organic compounds (VOCs) on ozone (O3) pollution is of wide concern particularly in highly industrialized megacities. In this study, O3, nitrogen oxides (NOx) and VOCs were measured at an urban site in the Pearl River Delta region during the 2018 Chinese National Day Holidays and two after-holiday periods (one with ozone pollution and another without). O3 pollution occurred throughout the 7-day holidays even industrial emissions of VOCs were passively reduced due to temporary factory shutdowns, and the toluene to benzene ratios dropped from ∼10 during non-holidays to ∼5 during the holidays. Box model (AtChem2-MCM) simulations with the input of observation data revealed that O3 formation was all VOC-limited, and alkenes had the highest relative incremental reactivity (RIR) during the holiday and non-holiday O3 episodes while aromatics had the highest RIR during the non-pollution period. Box model also demonstrated that even aromatics decreased proportionally to levels with near-zero contributions of industrial aromatic solvents, O3 concentrations would only decrease by less than 20% during the holiday and non-holiday O3 episodes and ozone pollution in the periods could not be eliminated. The results imply that controlling emissions of industrial aromatic solvents might be not enough to eliminate O3 pollution in the region, and more attention should be paid to anthropogenic reactive alkenes. Isoprene and formaldehyde were among the top 3 species by RIRs in all the three pollution and non-pollution periods, suggesting substantial contribution to O3 formation from biogenic VOCs.
Collapse
Affiliation(s)
- Jun Wang
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yanli Zhang
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; CAS Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Zhenfeng Wu
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shilu Luo
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Song
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
| | - Xinming Wang
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; CAS Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| |
Collapse
|
13
|
Chen Y, Zhou Y, Zhang H, Wang C, Wang X. Spatiotemporal variations of surface ozone and its influencing factors across Tibet: A Geodetector-based study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 813:152651. [PMID: 34954172 DOI: 10.1016/j.scitotenv.2021.152651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/22/2021] [Accepted: 12/20/2021] [Indexed: 06/14/2023]
Abstract
Reasons regarding surface ozone formation and distribution in remote regions is limited. Tibet is an important remote area on Earth, with various climates and extremely high elevation (average ~ 4000 m), which makes it a good place to study the spatiotemporal distribution of surface ozone and explore the causes. Based on ground monitoring data from 18 stations on Tibet between 2015 and 2019, the annual, seasonal, monthly, and diurnal variations of surface ozone were analyzed. The annual mean values (60.7-72.5 μg/m3) presented an increasing trend during the past five years, with seasonal concentrations of surface ozone higher in spring than in winter. Spatially, both the ground observations and high-resolution remote sensing data indicated that the surface ozone was relatively high in the southwest regions of Tibet, and low in the southeast and northeast areas. Geodetector analysis found that relative humidity (RH), normalized difference vegetation index (NDVI), and solar radiation (SR) were the top three individual factors affecting surface ozone distribution, while NO2, PM10, and PM2.5 showed less influence. All influencing factors showed an improvement through the two-factor interaction. The associations of RH∩PM10 (q = 0.77), RH∩NDVI (q = 0.72), and NDVI∩SR (q = 0.73) exhibited a strong impact on surface ozone distribution, suggesting that places with sparse vegetation cover, dry climate and strong SR would usually cause high atmospheric ozone burden. This could also explain why concentrations of surface ozone continue to increase in some remote areas worldwide with ecological deterioration and desertification.
Collapse
Affiliation(s)
- Yan Chen
- State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yunqiao Zhou
- State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China.
| | - Huifang Zhang
- Tibetan Ecology and Environment Monitoring Center, Lhasa 850000, China
| | - Caihong Wang
- Tibetan Ecology and Environment Monitoring Center, Lhasa 850000, China
| | - Xiaoping Wang
- State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| |
Collapse
|
14
|
Zhou X, Strezov V, Jiang Y, Kan T, Evans T. Temporal and spatial variations of air pollution across China from 2015 to 2018. J Environ Sci (China) 2022; 112:161-169. [PMID: 34955200 DOI: 10.1016/j.jes.2021.04.025] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 04/21/2021] [Accepted: 04/23/2021] [Indexed: 05/16/2023]
Abstract
This study investigated concentrations of PM2.5, PM10, SO2, NO2, CO and O3, and air quality index (AQI) values across 368 cities in mainland China during 2015-2018. The study further examined relationships of air pollution status with local industrial capacities and vehicle possessions. Strong correlations were found between industrial capacities (coal, pig iron, crude steel and rolled steel) and air pollution levels. Although statistical and significant reductions of PM2.5, PM10, SO2, NO2, CO and AQI values were observed in response to various laws and regulations in industrial sectors, both particle and gaseous pollutants still had annual average concentrations above recommended limits. In order to further reduce air pollution, more efforts can be done to control traffic emissions caused by minicars and heavy trucks, which was revealed after investigating 16 vehicle types. This was also consistent with the apparent air quality improvement during the COVID-19 lockdown period in China in 2020, despite industrial operations being still active at full capacities.
Collapse
Affiliation(s)
- Xiaoteng Zhou
- ARC Research Hub for Computational Particle Technology, Macquarie University, Sydney, New South Wales 2109, Australia; Department of Earth and Environmental Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales 2109, Australia.
| | - Vladimir Strezov
- ARC Research Hub for Computational Particle Technology, Macquarie University, Sydney, New South Wales 2109, Australia; Department of Earth and Environmental Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Yijiao Jiang
- ARC Research Hub for Computational Particle Technology, Macquarie University, Sydney, New South Wales 2109, Australia; School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Tao Kan
- Department of Earth and Environmental Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Tim Evans
- ARC Research Hub for Computational Particle Technology, Macquarie University, Sydney, New South Wales 2109, Australia; Department of Earth and Environmental Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales 2109, Australia
| |
Collapse
|
15
|
Zhang J, Liu Q, Chen L, Li H, Zhao R, Huang X, Zhang W, Wang Y. Interannual evolution of elemental carbon-containing particles in winter in the atmosphere of Chengdu, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 804:150133. [PMID: 34509847 DOI: 10.1016/j.scitotenv.2021.150133] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 08/29/2021] [Accepted: 08/31/2021] [Indexed: 06/13/2023]
Abstract
As one of the important components of PM2.5, elemental carbon (EC) particles could quickly mix with other organic or inorganic species after entering the atmosphere, which directly affected its climate and environmental effects and impact on human health. In order to study the mixture and evolution of EC particles in the atmosphere, this study used single particle aerosol mass spectrometry to measure the EC-containing particles in three winters (2016/17, 2018/19 and 2019/20) in Chengdu, one of the most polluted megacities in China. The results showed that the average mass concentrations of PM2.5 in the three winters were 121 ± 58 μg m-3, 90 ± 32 μg m-3 and 79 ± 38 μg m-3, respectively, and the mass ratios of PM2.5/PM10 were 0.71, 0.78 and 0.78, respectively. EC-containing particles accounted for 40.7%-54.7% of all particles and could be divided into six categories: EC-dust (EC-D), fresh EC (ECf), EC-organic carbon (EC-OC), EC-nitrate (EC-N), EC-sulfate (EC-S) and EC-sulfate-nitrate (EC-SN). From winter 2016/17 to 2019/20, the contributions of EC-N and EC-OC particles increased, while the contributions of EC-S and EC-SN particles decreased. Meanwhile, EC-N particles became increasingly important for the formation of heavy pollution. Photochemical and liquid-phase processes dominated the formation of EC-N and EC-S particles, respectively, while the dominant formation mechanism of EC-SN particles changed from photochemical to liquid-phase processes from winter 2016/17 to 2019/20. Total EC and EC-S particles in Chengdu were mainly transported from the east and southeast areas to Chengdu, while ECf particles mainly derived from local emissions. Compared with the previous two winters, the potential source areas of the EC-containing particles in winter 2019/20 reduced significantly. These findings provide new insight into the atmospheric behavior of urban particles and are important reference for the study of EC particles in other regions in the world, especially in urban areas.
Collapse
Affiliation(s)
- Junke Zhang
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
| | - Qin Liu
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
| | - Luyao Chen
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
| | - Huan Li
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
| | - Rui Zhao
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China.
| | - Xiaojuan Huang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
| | - Wei Zhang
- Sichuan Environmental Monitoring Center, Chengdu 610074, China
| | - Yuesi Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| |
Collapse
|
16
|
Yan M, Gong J, Liu Q, Li W, Duan X, Cao S, Li S, He L, Yin Z, Lin W, Zhang JJ. The effects of indoor and outdoor air pollution on the prevalence of adults' respiratory diseases in four Chinese cities: a comparison between 2017-2018 and 1993-1996. J Thorac Dis 2021; 13:4560-4573. [PMID: 34422382 PMCID: PMC8339734 DOI: 10.21037/jtd-20-2121] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 04/25/2021] [Indexed: 11/24/2022]
Abstract
Background Over the past decades, both ambient and household air pollution have changed in several aspects, including the emission sources and the concentrations of pollutants, in many Chinese cities. It is unknown whether these changes are associated with changes in health conditions, especially given changes in other factors due to rapid economic growth. Methods Two cross-sectional surveys were conducted in two periods spanning more than twenty years (1993–1996 vs. 2017–2018) in four Chinese cities of Chongqing, Wuhan, Lanzhou, and Guangzhou. Data were collected regarding adults’ respiratory disease, smoking status, education, occupation, and household characteristics. Ambient air pollution data were obtained for each study. We first used logistic regression models to construct the district-specific adjusted disease prevalences. In the second stage, first-difference regression models were employed to examine whether the change in respiratory diseases prevalences was associated with the change in outdoor air pollution and indoor air pollution surrogates. Results A total of 7,557 and 9,974 households were participating in Period 1 (1993–1996) and Period 2 (2017–2018), respectively. Compared to Period 1, we found substantial reductions in the ambient air pollution concentrations, and a suggestive improvement in cooking-related indoor air pollution in Period 2. We observed decreases in the district-specific covariate-adjusted prevalences of both asthma and chronic bronchitis among participants, with an average reduction of 3.6% (range: 0.0% to 24.3%). From Period 1 to Period 2, one percent decrease in the proportion of cooking with coal was associated with a 19.0% (95% CI, 0.96–37.04%) decrease in the prevalence of males’ chronic bronchitis and a 1.86% (0.69–3.04%) increase in the prevalence of females’ asthma. Little evidence was observed regarding the potential health benefits associated with the decreases in ambient air pollution levels. Conclusions The substantial reduction in household use of coal for cooking might be an important contributor to the decrease in adults’ respiratory disease prevalence from 1993–1996 to 2017–2019 in four Chinese cities. Changes in this indoor air pollution source, along with other risk factors for respiratory diseases, may have masked respiratory health benefits associated with reductions in outdoor air pollution levels.
Collapse
Affiliation(s)
- Meilin Yan
- Beijing Innovation Center for Engineering Science and Advanced Technology, State Key Joint Laboratory for Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, And Center for Environment and Health, Peking University, Beijing, China
| | - Jicheng Gong
- Beijing Innovation Center for Engineering Science and Advanced Technology, State Key Joint Laboratory for Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, And Center for Environment and Health, Peking University, Beijing, China
| | - Qin Liu
- School of Public Health and Management, Research Center for Medicine and Social Development, Collaborative Innovation Center of Social Risks Governance in Health, Chongqing Medical University, Chongqing, China
| | - Wenyan Li
- School of Public Health and Management, Research Center for Medicine and Social Development, Collaborative Innovation Center of Social Risks Governance in Health, Chongqing Medical University, Chongqing, China
| | - Xiaoli Duan
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, China
| | - Suzhen Cao
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, China
| | - Sai Li
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, China
| | - Lingyan He
- Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Zixuan Yin
- Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Weiwei Lin
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, China
| | - Junfeng Jim Zhang
- Global Health Research Center, Duke Kunshan University, Kunshan, China.,Nicholas School of the Environment and Duke Global Health Institute, Duke University, Durham, USA.,Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| |
Collapse
|
17
|
Chung CY, Yang J, He J, Yang X, Hubbard R, Ji D. An investigation into the impact of variations of ambient air pollution and meteorological factors on lung cancer mortality in Yangtze River Delta. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 779:146427. [PMID: 33752019 DOI: 10.1016/j.scitotenv.2021.146427] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 03/06/2021] [Accepted: 03/08/2021] [Indexed: 06/12/2023]
Abstract
Lung cancer (LC) mortality, as one of the top cancer deaths in China, has been associated with increased levels of exposure to ambient air pollutants. In this study, different lag times on weekly basis were applied to study the association of air pollutants (PM2.5, PM10, and NO2) and LC mortality in Ningbo, and in subpopulations at different age groups and genders. Furthermore, seasonal variations of pollutant concentrations and meteorological variables (temperature, relative humidity, and wind speed) were analysed. A generalised additive model (GAM) using Poisson regression was employed to estimate the effect of single pollutant model on LC mortality in Yangtze River Delta using Ningbo as a case study. It was reported that there were statistically significant relationships between lung cancer mortality and air pollutants. Increases of 6.2% (95% confidence interval [CI]: 0.2% to 12.6%) and 4.3% (95% CI: 0.1% to 8.5%) weekly total LC mortality with a 3-week lag time were linked to each 10 μg/m3 increase of weekly average PM2.5 and PM10 respectively. The association of air pollutants (PM2.5, PM10 and NO2) and LC mortality with a 3-week lag time was also found statistically significant during periods of low temperature (T < 18 °C), low relative humidity (H < 73.7%) and low wind speed (u < 2.8 m/s), respectively. The female population was found to be more susceptible to the exposure to air pollution than the male population. In addition, the population with an age of 50 years or above was shown to be more sensitive to ambient air pollutant. These outcomes indicated that increased risk of lung cancer mortality was evidently linked to exposure to ambient air pollutant on a weekly basis. The impact of weekly variation on the LC mortality and air pollutant levels should be considered in air pollution-related health burden analysis.
Collapse
Affiliation(s)
- Chee Yap Chung
- Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo 315100, PR China
| | - Jie Yang
- School of Mathematical Sciences, University of Nottingham Ningbo China, Ningbo 315100, PR China.
| | - Jun He
- Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo 315100, PR China
| | - Xiaogang Yang
- Department of Mechanical, Materials and Manufacturing Engineering, University of Nottingham Ningbo China, Ningbo 315100, PR China
| | - Richard Hubbard
- School of Medicine, University of Nottingham, Nottingham NG7 2UH, UK
| | - Dongsheng Ji
- State key laboratory of Atmospheric Boundary Layer Physics and Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100191, PR China
| |
Collapse
|
18
|
Analysis of Ozone Pollution Characteristics and Influencing Factors in Northeast Economic Cooperation Region, China. ATMOSPHERE 2021. [DOI: 10.3390/atmos12070843] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The increase in tropospheric ozone (O3) concentration has become one of the factors restricting urban development. This paper selected the important economic cooperation areas in Northeast China as the research object and collected the hourly monitoring data of pollutants and meteorological data in 11 cities from 1 January 2015 to 31 December 2019. The temporal and spatial variation trend of O3 concentration and the effects of meteorological factors and other pollutants, including CO (carbon monoxide), SO2 (sulfur dioxide), NO2 (nitrogen dioxide), and PM2.5 and PM10 (PM particles with aerodynamic diameters less than 2.5 μm and 10 μm) on ozone concentration were analyzed. At the same time, the variation period of O3 concentration was further analyzed by Morlet wavelet analysis. The results showed that the O3 pollution in the study area had a significant spatial correlation. The spatial distribution showed that the O3 concentration was relatively high in the south and low in the northeast. Seasonally, the O3 concentration was the highest in spring, followed by summer, and the lowest in winter. The diurnal variation of O3 concentration presented a “single peak” pattern. O3 concentration had a significant positive correlation with temperature, sunshine duration, and wind speed and a significant anticorrelation with CO, NO2, SO2, and PM2.5 concentration. Under the time scale of a = 9, 23, O3 had significant periodic fluctuation, which was similar to those of wind speed and temperature.
Collapse
|
19
|
Yin M, Zhang X, Li Y, Fan K, Li H, Gao R, Li J. Ambient ozone pollution at a coal chemical industry city in the border of Loess Plateau and Mu Us Desert: characteristics, sensitivity analysis and control strategies. PeerJ 2021; 9:e11322. [PMID: 33987008 PMCID: PMC8086580 DOI: 10.7717/peerj.11322] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 03/31/2021] [Indexed: 11/20/2022] Open
Abstract
In this study, ambient ozone (O3) pollution characteristics and sensitivity analysis were carried out in Yulin, a city in the central area of the Loess Plateau during 2017 to 2019 summer. O3 concentrations increased for 2017 to 2019. Correlation and statistics analysis indicated high temperature (T > 25 °C, low relative humidity (RH < 60%), and low wind speed (WS < 3 m/s) were favorable for O3 formation and accumulation, and the O3 pollution days (MDA8 O3 > 160 µg/m3) were predominantly observed when the wind was traveling from the easterly and southerly. O3 concentration in urban area of Yulin was higher than that in background. The pollution air masses from Fenwei Plain increase the level and duration of O3 pollution. In order to clarify the formation mechanism and source of O3, online measurements of volatile organic compounds (VOCs) were conducted from 7 July to 10 August in 2019. The average of VOCs concentration was 26 ± 12 ppbv, and large amounts of alkenes followed by aromatics, characteristic pollutants of the coal chemical industry, were detected in the ambient air. To further measure the sensitivity, the observation-based model (OBM) simulation was conducted. Empirical Kinetic Modeling Approach (EKMA) plot and relative incremental reactivity (RIR) value indicated Yulin located on VOCs-limited regime. That implied a slight decrease of NOx may increase O3 concentration. When the emission reduction ratio of anthropogenic VOCs/NOx higher than 1:1, the O3 will decrease. O3 control strategies analysis shows that the O3 targets of 5% and 10% O3 concentration reductions are achievable through precursor control, but more effort is needed to reach the 30% and 40% reduction control targets.
Collapse
Affiliation(s)
- Manfei Yin
- College of Resource and Environment Engineering, Guizhou University, Guiyang, China.,State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Xin Zhang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China.,Environment Research Institute, Shandong University, Jinan, China
| | - Yunfeng Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China.,Environment Research Institute, Shandong University, Jinan, China
| | - Kai Fan
- Yulin Municipal Ecology and Environment Bureau, Yulin, China
| | - Hong Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Rui Gao
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Jinjuan Li
- College of Resource and Environment Engineering, Guizhou University, Guiyang, China
| |
Collapse
|
20
|
Guo W, Chen B, Li G, Liu M, Liu X, Chen Q, Zhang X, Li S, Chen S, Feng W, Zhang R, Chen M, Shi T. Ambient PM 2.5 and Related Health Impacts of Spontaneous Combustion of Coal and Coal Gangue. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:5763-5771. [PMID: 33818073 DOI: 10.1021/acs.est.1c00150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Coal and coal gangue spontaneous combustion (CGSC) occurs globally, causing significant environmental pollution. However, its emissions are poorly quantified and are overlooked in global or regional air pollutant emission inventories in previous studies, resulting in the underestimation of its impacts on climate, environment, and public health. This study quantified the emissions of various air pollutants originating from CGSC in Wuhai, a city in China, investigated emission characteristics, and estimated the contribution of CGSC emissions to fine particulate matter (PM2.5) air pollution and related health impacts on a regional scale. The results revealed that the CGSC-related PM2.5 emissions were approximately 4643 t a-1 (95% confidence interval (95% CI): 721; 10447), accounting for 26.3% of the total PM2.5 emissions. Alkanes, alkenes, and aromatics accounted for 69.4, 17.9, and 2.9%, respectively, of the total emissions of volatile organic compounds (VOCs). Due to CGSC emissions, the ambient PM2.5 concentration in Wuhai increased by 5.7 μg m-3 on average, while the nitrate concentration decreased. The number of premature deaths caused by exposure to ambient PM2.5 associated with CGSC reached 381 (95% CI: 290; 452) in Wuhai and surrounding cities in 2017. Urgent control strategies and engineering techniques are needed to mitigate CGSC to protect public health.
Collapse
Affiliation(s)
- Wenkai Guo
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, P. R. China
| | - Bing Chen
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, P. R. China
| | - Guangyao Li
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, P. R. China
| | - Mingyue Liu
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, P. R. China
| | - Xiao Liu
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, P. R. China
| | - Qiang Chen
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, P. R. China
| | - Xiaorui Zhang
- Department of Geography, State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong SAR 999077, P. R. China
| | - Shixue Li
- Graduate School of Environmental Science, Hokkaido University, Sapporo 060-0810, Japan
| | - Siyu Chen
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, P. R. China
| | - Wuhu Feng
- School of Earth and Environment, University of Leeds, Leeds LS2 9JT, United Kingdom
- National Centre for Atmospheric Science, University of Leeds, Leeds LS2 9JT, United Kingdom
| | - Ruixin Zhang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, P. R. China
| | - Mei Chen
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730000, P. R. China
| | - Tenglong Shi
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, P. R. China
| |
Collapse
|
21
|
Xue T, Zheng Y, Geng G, Xiao Q, Meng X, Wang M, Li X, Wu N, Zhang Q, Zhu T. Estimating Spatiotemporal Variation in Ambient Ozone Exposure during 2013-2017 Using a Data-Fusion Model. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:14877-14888. [PMID: 33174716 DOI: 10.1021/acs.est.0c03098] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Since 2013, clean-air actions in China have reduced ambient concentrations of PM2.5. However, recent studies suggest that ground surface O3 concentrations increased over the same period. To understand the shift in air pollutants and to comprehensively evaluate their impacts on health, a spatiotemporal model for O3 is required for exposure assessment. This study presents a data-fusion algorithm for O3 estimation that combines in situ observations, satellite remote sensing measurements, and model results from the community multiscale air quality model. Performance of the algorithm for O3 estimation was evaluated by five-fold cross-validation. The estimates are highly correlated with the in situ observations of the maximum daily 8 h averaged O3 (R2 = 0.70). The mean modeling error (measured using the root-mean-squared error) is 26 μg/m3, which accounts for 29% of the mean level. We also found that satellite O3 played a key role to improve model performance, particularly during warm months. The estimates were further used to illustrate spatiotemporal variation in O3 during 2013-2017 for the whole country. In contrast to the reduced trend of PM2.5, we found that the population-weighted O3 mean increased from 86 μg/m3 in 2013 to 95 μg/m3 in 2017, with a rate of 2.07 (95% CI: 1.65, 2.48) μg/m3 per year at the national level. This increased trend in O3 suggests that it is becoming an important contributor to the burden of diseases attributable to air pollutants in China. The developed method and the results generated from this study can be used to support future health-related studies in China.
Collapse
Affiliation(s)
- Tao Xue
- Institute of Reproductive and Child Health / Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Yixuan Zheng
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
- Center of Air Quality Simulation and System Analysis, Chinese Academy for Environmental Planning, Beijing 100012, China
| | - Guannan Geng
- School of Environment, Tsinghua University, Beijing 100084, China
| | - Qingyang Xiao
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Xia Meng
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, & NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Meng Wang
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, New York 14214, United States
| | - Xin Li
- Department of Environmental Science and Engineering, Beijing Technology and Business University, Beijing 100048, China
| | - Nana Wu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Tong Zhu
- BIC-ESAT and SKL-ESPC, Peking University, Beijing 100871, China
| |
Collapse
|
22
|
Gong C, Liao H, Zhang L, Yue X, Dang R, Yang Y. Persistent ozone pollution episodes in North China exacerbated by regional transport. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 265:115056. [PMID: 32593927 DOI: 10.1016/j.envpol.2020.115056] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 05/31/2020] [Accepted: 06/16/2020] [Indexed: 05/16/2023]
Abstract
Summertime ozone (O3) concentrations over China continue to increase although the governmental Clear Air Actions have been carried out since 2013. The worst O3 pollution is confronted over North China Plain. Ozone polluted days (with observed regionally-averaged maximum daily 8-h average (MDA8) O3 concentrations exceeding 80 ppbv) in May-July in North China increased from 35 days in year 2014 to 56 days in year 2018, and persistent O3 pollution episodes that lasted for 5 days or longer (OPEs5) contributed 14.3% and 66.1% to those O3 polluted days in 2014 and 2018, respectively. Model simulations suggest that O3 transport from central eastern China (including Shandong, Henan, Jiangsu and Anhui Provinces) contributes 36% of the enhanced O3 concentrations in North China during OPEs5 relative to the seasonal mean. We find that emission control of volatile organic compounds in central eastern China is most effective to alleviate intensity of OPEs5 in North China.
Collapse
Affiliation(s)
- Cheng Gong
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hong Liao
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, Jiangsu, China.
| | - Lin Zhang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China
| | - Xu Yue
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, Jiangsu, China
| | - Ruijun Dang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yang Yang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, Jiangsu, China
| |
Collapse
|
23
|
Modeling Ozone Source Apportionment and Performing Sensitivity Analysis in Summer on the North China Plain. ATMOSPHERE 2020. [DOI: 10.3390/atmos11090992] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
In recent years, air quality issues due to fine particulate matter have been sufficiently treated. However, ozone (O3) has now become the primary pollutant in summer on the North China Plain (NCP). In this study, a three-dimensional chemical transport model (the Nested Air Quality Prediction Model System, NAQPMS) coupled with an online source apportionment module was applied to investigate the sources of O3 pollution over the NCP. Generally, the NAQPMS adequately captured the observed spatiotemporal features of O3 during the period of July 1st to August 31st in 2017 on the NCP. The results of the source apportionment indicated that the contributions of local emissions and transport from the NCP accounted for the largest proportion of O3, with magnitudes of 25% and 39%, respectively. Compared with those in the average monthly results, the local contribution and regional transport during O3 episodes on the NCP increased by 7% and 10%, respectively. Based on sensitivity tests, two thresholds of the sensitivity indicator P(H2O2)/P(HNO3) were detected, at 0.08 and 0.2. Ozone formation in the urban sites of Beijing, Tianjin, and the southern part of Hebei Province was controlled by VOCs, while the other sites were mainly controlled by NOX. Biogenic emissions contributed approximately 18% to O3 formation in July in the southwestern part of Hebei Province.
Collapse
|
24
|
Cao R, Li B, Wang Z, Peng ZR, Tao S, Lou S. Using a distributed air sensor network to investigate the spatiotemporal patterns of PM 2.5 concentrations. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 264:114549. [PMID: 32408078 DOI: 10.1016/j.envpol.2020.114549] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 04/04/2020] [Accepted: 04/04/2020] [Indexed: 06/11/2023]
Abstract
Spatiotemporal variations in PM2.5 are a key factor affecting personal pollution exposure levels in urban areas. However, fixed-site monitoring stations are so sparsely distributed that they hardly capture the dynamic and fine-scale variations in PM2.5 in urban areas with complex geographical features and urban forms. Recently, a distributed air sensor network (DASN) was deployed in Dezhou city, China, to monitor fine-scale air pollution information and obtain deep insight into variations in PM2.5. Based on the data collected by the DASN, this paper investigated the spatiotemporal patterns of PM2.5 using the time-series clustering method. The results demonstrated that there were four stages of PM2.5 daily variations, i.e., accumulation, continuous pollution, dispersion, and cleaning. Generally, the stage of dispersion occurred more rapidly than the stage of accumulation, and PM2.5 accumulated easily in warm and humid weather with low wind speeds. However, the stage of dispersion was affected mainly by high wind speeds and precipitation. Additionally, the results suggested that four variation stages did not strictly correspond to seasonal divisions. The spatial distributions of PM2.5 revealed that the main pollution source was located in a southeastern industrial park, which exhibited a significant impact throughout the four stages. Considering both the temporal and spatial characteristics of PM2.5, this study successfully identified pollution hotspots and confirmed the effect of industrial parks. The study demonstrates that the DASN has high prospective applicability for assessing the fine-scale spatial distribution of PM2.5, and the time-series clustering method can also assist environmental researchers in further exploring the spatiotemporal characteristics of urban air pollution.
Collapse
Affiliation(s)
- Rong Cao
- Center for Intelligent Transportation Systems and Unmanned Aerial Systems Applications, State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Bai Li
- Center for Intelligent Transportation Systems and Unmanned Aerial Systems Applications, State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Zhanyong Wang
- College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350108, China.
| | - Zhong-Ren Peng
- International Center for Adaptation Planning and Design (iAdapt), School of Landscape Architecture and Planning, College of Design, Construction, and Planning, University of Florida, P.O. Box 115706, Gainesville, FL, 32611-5706, USA
| | - Shikang Tao
- State Environmental Protection Key Laboratory of the Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, 200233, China
| | - Shengrong Lou
- State Environmental Protection Key Laboratory of the Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, 200233, China
| |
Collapse
|
25
|
Guo P, Umarova AB, Luan Y. The spatiotemporal characteristics of the air pollutants in China from 2015 to 2019. PLoS One 2020; 15:e0227469. [PMID: 32822345 PMCID: PMC7444542 DOI: 10.1371/journal.pone.0227469] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 08/05/2020] [Indexed: 11/25/2022] Open
Abstract
China’s rapid industrialization and urbanization have led to poor air quality, and air pollution has caused great concern among the Chinese public. Most analyses of air pollution trends in China are based on model simulations or satellite data. Studies using field observation data and focusing on the latest data from environmental monitoring stations covering the whole country to assess the latest trends of different pollutants in different regions are relatively rare. The State Council of China promulgated the toughest-ever Air Pollution Prevention and Control Action Plan (Action Plan) in 2013. This led to a major improvement in air quality. We use the hourly Air Quality Index (AQI) and mass concentrations of PM2.5, PM10, CO, NO2, O3, and SO2 in 362 cities from 2015 to 2019, obtained from the Ministry of Ecology and Environment, to study their temporal and spatial changes and assess the effectiveness of the policy on the atmospheric environment since its promulgation and implementation. We found that the national and regional air quality in China continues to improve, with PM2.5, PM10, AQI, CO, and SO2 exhibiting negative trends. However, O3 and NO2 pollution is an urgent problem that needs to be solved and the current control strategy for PM2.5 will only partially reduce the PM2.5 pollution in the western region. Although the implementation of "Action Plan" measures has effectively improved air quality, China’s air pollution is still serious and far from the WHO standard. Implementing measures for continuous and effective emissions control is still a top priority.
Collapse
Affiliation(s)
- Peng Guo
- Department of Soil, Moscow State University, Moscow, Russian Federation
- * E-mail:
| | | | - Yunqi Luan
- Department of Soil, Moscow State University, Moscow, Russian Federation
| |
Collapse
|
26
|
Liu P, Song H, Wang T, Wang F, Li X, Miao C, Zhao H. Effects of meteorological conditions and anthropogenic precursors on ground-level ozone concentrations in Chinese cities. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 262:114366. [PMID: 32443214 DOI: 10.1016/j.envpol.2020.114366] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 02/29/2020] [Accepted: 03/10/2020] [Indexed: 06/11/2023]
Abstract
Ground-level ozone pollution has negative impacts on human health and vegetation and has increased rapidly across China. Various factors are implicated in the formation of ozone (e.g., meteorological factors, anthropogenic emissions), but their relative individual impact and the impact of interactions between these factors remains unclear. This study quantified the influence of specific meteorological conditions and anthropogenic precursor emissions and their interactions on ozone concentrations in Chinese cities using the geographic detector model (GeoDetector). Results revealed that the impacts of meteorological and anthropogenic factors and their interactions on ozone concentrations varied significantly at different spatial and temporal scales. Temperature was the dominant driver at the annual time scale, explaining 40% (q = 0.4) of the ground-level ozone concentration. Anthropogenic precursors and meteorological conditions had comparable effects on ozone concentrations in summer and winter in northern China. Interactions between all the factors can enhance effects. The interaction between meteorological factors and anthropogenic precursors had the strongest impact in summer. The results can be used to enhance our understanding of ozone pollution, to improve ozone prediction models, and to formulate pollution control measures.
Collapse
Affiliation(s)
- Pengfei Liu
- Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng, 475001, China; Institute of Urban Big Data, College of Environment and Planning, Henan University, Kaifeng, Henan, 475004, China
| | - Hongquan Song
- Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, Henan, 475004, China; Henan Key Laboratory of Integrated Air Pollution Control and Ecological Security, Henan University, Kaifeng, Henan, 475004, China; Institute of Urban Big Data, College of Environment and Planning, Henan University, Kaifeng, Henan, 475004, China.
| | - Tuanhui Wang
- Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, Henan, 475004, China; Institute of Urban Big Data, College of Environment and Planning, Henan University, Kaifeng, Henan, 475004, China
| | - Feng Wang
- Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, Henan, 475004, China; Institute of Urban Big Data, College of Environment and Planning, Henan University, Kaifeng, Henan, 475004, China
| | - Xiaoyang Li
- Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, Henan, 475004, China; Institute of Urban Big Data, College of Environment and Planning, Henan University, Kaifeng, Henan, 475004, China
| | - Changhong Miao
- Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng, 475001, China
| | - Haipeng Zhao
- Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, Henan, 475004, China; Institute of Urban Big Data, College of Environment and Planning, Henan University, Kaifeng, Henan, 475004, China
| |
Collapse
|
27
|
Tan Z, Hofzumahaus A, Lu K, Brown SS, Holland F, Huey LG, Kiendler-Scharr A, Li X, Liu X, Ma N, Min KE, Rohrer F, Shao M, Wahner A, Wang Y, Wiedensohler A, Wu Y, Wu Z, Zeng L, Zhang Y, Fuchs H. No Evidence for a Significant Impact of Heterogeneous Chemistry on Radical Concentrations in the North China Plain in Summer 2014. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:5973-5979. [PMID: 32343120 PMCID: PMC7240937 DOI: 10.1021/acs.est.0c00525] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 04/27/2020] [Accepted: 04/28/2020] [Indexed: 05/28/2023]
Abstract
The oxidation of nitric oxide to nitrogen dioxide by hydroperoxy (HO2) and organic peroxy radicals (RO2) is responsible for the chemical net ozone production in the troposphere and for the regeneration of hydroxyl radicals, the most important oxidant in the atmosphere. In Summer 2014, a field campaign was conducted in the North China Plain, where increasingly severe ozone pollution has been experienced in the last years. Chemical conditions in the campaign were representative for this area. Radical and trace gas concentrations were measured, allowing for calculating the turnover rates of gas-phase radical reactions. Therefore, the importance of heterogeneous HO2 uptake on aerosol could be experimentally determined. HO2 uptake could have suppressed ozone formation at that time because of the competition with gas-phase reactions that produce ozone. The successful reduction of the aerosol load in the North China Plain in the last years could have led to a significant decrease of HO2 loss on particles, so that ozone-forming reactions could have gained importance in the last years. However, the analysis of the measured radical budget in this campaign shows that HO2 aerosol uptake did not impact radical chemistry for chemical conditions in 2014. Therefore, reduced HO2 uptake on aerosol since then is likely not the reason for the increasing number of ozone pollution events in the North China Plain, contradicting conclusions made from model calculations reported in the literature.
Collapse
Affiliation(s)
- Zhaofeng Tan
- Institute
of Energy and Climate Research, IEK-8: Troposphere, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 100871 Beijing, China
| | - Andreas Hofzumahaus
- Institute
of Energy and Climate Research, IEK-8: Troposphere, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 100871 Beijing, China
| | - Keding Lu
- International
Joint Laboratory for Regional Pollution Control, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 100871 Beijing, China
- State
Key Joint Laboratory of Environmental Simulation and Pollution Control,
College of Environmental Sciences and Engineering, Peking University, 100871 Beijing, China
| | - Steven S. Brown
- Chemical
Sciences Division, NOAA Earth System Research
Laboratory, Boulder, Colorado 80309, United States
- Department
of Chemistry, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Frank Holland
- Institute
of Energy and Climate Research, IEK-8: Troposphere, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 100871 Beijing, China
| | - Lewis Gregory Huey
- School
of Earth and Atmospheric Sciences, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
| | - Astrid Kiendler-Scharr
- Institute
of Energy and Climate Research, IEK-8: Troposphere, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 100871 Beijing, China
| | - Xin Li
- International
Joint Laboratory for Regional Pollution Control, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 100871 Beijing, China
- State
Key Joint Laboratory of Environmental Simulation and Pollution Control,
College of Environmental Sciences and Engineering, Peking University, 100871 Beijing, China
| | - Xiaoxi Liu
- School
of Earth and Atmospheric Sciences, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
| | - Nan Ma
- Leibniz
Institute for Tropospheric Research, 04318 Leipzig, Germany
| | - Kyung-Eun Min
- Cooperative
Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Franz Rohrer
- Institute
of Energy and Climate Research, IEK-8: Troposphere, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 100871 Beijing, China
| | - Min Shao
- International
Joint Laboratory for Regional Pollution Control, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 100871 Beijing, China
- State
Key Joint Laboratory of Environmental Simulation and Pollution Control,
College of Environmental Sciences and Engineering, Peking University, 100871 Beijing, China
| | - Andreas Wahner
- Institute
of Energy and Climate Research, IEK-8: Troposphere, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 100871 Beijing, China
| | - Yuhang Wang
- School
of Earth and Atmospheric Sciences, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
| | | | - Yusheng Wu
- State
Key Joint Laboratory of Environmental Simulation and Pollution Control,
College of Environmental Sciences and Engineering, Peking University, 100871 Beijing, China
| | - Zhijun Wu
- International
Joint Laboratory for Regional Pollution Control, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 100871 Beijing, China
- State
Key Joint Laboratory of Environmental Simulation and Pollution Control,
College of Environmental Sciences and Engineering, Peking University, 100871 Beijing, China
| | - Limin Zeng
- International
Joint Laboratory for Regional Pollution Control, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 100871 Beijing, China
- State
Key Joint Laboratory of Environmental Simulation and Pollution Control,
College of Environmental Sciences and Engineering, Peking University, 100871 Beijing, China
| | - Yuanhang Zhang
- International
Joint Laboratory for Regional Pollution Control, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 100871 Beijing, China
- State
Key Joint Laboratory of Environmental Simulation and Pollution Control,
College of Environmental Sciences and Engineering, Peking University, 100871 Beijing, China
- Beijing
Innovation Center for Engineering Science and Advanced Technology, Peking University, 100871 Beijing, China
- CAS Center for Excellence in Regional Atmospheric Environment, Chinese Academy of Science, 361000 Xiamen, China
| | - Hendrik Fuchs
- Institute
of Energy and Climate Research, IEK-8: Troposphere, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 52428 Jülich, Germany
- International
Joint Laboratory for Regional Pollution Control, 100871 Beijing, China
| |
Collapse
|
28
|
Wang Y, Xu K, Li S. The Functional Spatio-Temporal Statistical Model with Application to O 3 Pollution in Beijing, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17093172. [PMID: 32370183 PMCID: PMC7246770 DOI: 10.3390/ijerph17093172] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 04/19/2020] [Accepted: 04/28/2020] [Indexed: 11/16/2022]
Abstract
In recent years, with rapid industrialization and massive energy consumption, ground-level ozone (O3) has become one of the most severe air pollutants. In this paper, we propose a functional spatio-temporal statistical model to analyze air quality data. Firstly, since the pollutant data from the monitoring network usually have a strong spatial and temporal correlation, the spatio-temporal statistical model is a reasonable method to reveal spatial correlation structure and temporal dynamic mechanism in data. Secondly, effects from the covariates are introduced to explore the formation mechanism of ozone pollution. Thirdly, considering the obvious diurnal pattern of ozone data, we explore the diurnal cycle of O3 pollution using the functional data analysis approach. The spatio-temporal model shows great applicational potential by comparison with other models. With application to O3 pollution data of 36 stations in Beijing, China, we give explanations of the covariate effects on ozone pollution, such as other pollutants and meteorological variables, and meanwhile we discuss the diurnal cycle of ozone pollution.
Collapse
Affiliation(s)
- Yaqiong Wang
- Guanghua School of Management, Peking University, Beijing 100871, China; (Y.W.); (S.L.)
| | - Ke Xu
- School of Statistics, University of International Business and Economics, Beijing 100029, China
- Correspondence:
| | - Shaomin Li
- Guanghua School of Management, Peking University, Beijing 100871, China; (Y.W.); (S.L.)
| |
Collapse
|
29
|
Yang G, Liu Y, Li X. Spatiotemporal distribution of ground-level ozone in China at a city level. Sci Rep 2020; 10:7229. [PMID: 32350319 PMCID: PMC7190652 DOI: 10.1038/s41598-020-64111-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 03/20/2020] [Indexed: 01/24/2023] Open
Abstract
In recent years, ozone (O3) pollution in China has shown a worsening trend. Due to the vast territory of China, O3 pollution is a widespread and complex problem. It is vital to understand the current spatiotemporal distribution of O3 pollution in China. In this study, we collected hourly data on O3 concentrations in 338 cities from January 1, 2016, to February 28, 2019, to analyze O3 pollution in China from a spatiotemporal perspective. The spatial analysis showed that the O3 concentrations exceeded the limit in seven geographical regions of China to some extent, with more serious pollution in North, East, and Central China. The O3 concentrations in the eastern areas were usually higher than those in the western areas. The temporal analysis showed seasonal variations in O3 concentration, with the highest O3 concentration in the summer and the lowest in the winter. The weekend effect, which occurs in other countries (such as the USA), was found only in some cities in China. We also found that the highest O3 concentration usually occurred in the afternoon and the lowest was in the early morning. The comprehensive analysis in this paper could improve our understanding of the severity of O3 pollution in China.
Collapse
Affiliation(s)
- Guangfei Yang
- Institute of Systems Engineering, Dalian University of Technology, Dalian, China.
| | - Yuhong Liu
- Institute of Systems Engineering, Dalian University of Technology, Dalian, China
| | - Xianneng Li
- Institute of Systems Engineering, Dalian University of Technology, Dalian, China
| |
Collapse
|
30
|
Shu L, Wang T, Han H, Xie M, Chen P, Li M, Wu H. Summertime ozone pollution in the Yangtze River Delta of eastern China during 2013-2017: Synoptic impacts and source apportionment. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 257:113631. [PMID: 31784271 DOI: 10.1016/j.envpol.2019.113631] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 11/13/2019] [Accepted: 11/13/2019] [Indexed: 06/10/2023]
Abstract
Severe ozone (O3) pollution in China has magnified global concerns over the past years. Exploring the synoptic impacts and quantifying the source contributions are important for mitigating O3 pollution. This study focuses on the summertime O3 pollution over the Yangtze River Delta (YRD) in eastern China. We identify six predominant synoptic weather patterns (SWPs) over the YRD during 2013-2017 using the self-organising map (SOM) approach. We conduct O3 source attribution based on ozone source apportionment technology (OSAT) in a regional transport model. Surface O3 pollution is found to be sensitive to the predominant SWPs, including four O3-polluted types (northeasterly, northerly and southwesterly and anticyclone) and two O3-clean types (cyclone and the Meiyu front). The integrated influences of local chemistry and regional transport are important in O3 pollution under different SWPs. Daily maximum 8-h average (MDA8) O3 holds strong negative and positive correlations with relative humidity and sunshine duration, respectively. Among the six SWPs, the concentration and exceedance of MDA8 O3 reach their maxima under the northerly type on a regional average. Higher O3 levels, particularly under the northerly and northeasterly types, are primarily related to regional transport on various spatial scales. Local production accounts for the largest proportion of O3 sources under the anticyclone type. Under the southwesterly type, favourable weather conditions superimposed on regional transport lead to severe O3 pollution in coastal cities. The impact of super-regional transport is most pronounced along the eastern coast under the cyclone type. Source category analysis shows that transportation and industrial emissions play a prime role in surface O3 formation. This study illustrates the imperative implementation of joint emission control over eastern China to reduce O3 pollution.
Collapse
Affiliation(s)
- Lei Shu
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Tijian Wang
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China.
| | - Han Han
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Min Xie
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Pulong Chen
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Mengmeng Li
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Hao Wu
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| |
Collapse
|
31
|
Abstract
With the ground-level ozone pollution problem increasingly prominent in recent years in China, it is particularly important in basic researches on ozone contamination characteristics. In this study, 13 cities in Beijing-Tianjin-Hebei (BTH) area were examined to determine the characteristics of surface ozone (O3) from 2015 to 2018. Due to the photochemical oxidation of ozone precursors (such as nitrogen oxides and carbon monoxide) along with the presence of sunlight and characteristics of local emission sources, the O3 and oxidant (OX) concentrations showed obvious seasonal variation and daily variation. It implicated that the O3 concentrations reached the maximum during summer. The concentrations of O3 were higher at daytime than those measured at nighttime. The ozone weekend effect was estimated by the difference and deviation, which exhibited that the difference between weekday and weekend were related to the concentrations of ozone precursors and PM, vehicle emissions, and solar radiation. Moreover, the O3 concentrations decreased with the increase of other air pollutants by correlation analysis. The ozone pollution was easily formed at light and moderate polluted periods when compared to other air quality levels.
Collapse
|
32
|
Characteristics of Surface Ozone in Five Provincial Capital Cities of China during 2014–2015. ATMOSPHERE 2020. [DOI: 10.3390/atmos11010107] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Ozone (O3) pollution has become an increasing concern in China since elevated surface O3 concentrations were observed in recent years. In this study, five provincial cities (Beijing, Shanghai, Guangzhou, Xi’an, and Hefei) located in different regions of China were selected to study the spatiotemporal variations and affecting factors of O3 concentrations during 2014–2015. Beijing, Shanghai, and Guangzhou had suffered more severe O3 pollution, yet Beijing had the highest number of days that exceeded the Chinese MDA8 (maximum daily 8 h average) standard of 160 µg m−3. MDA8 O3 exhibited different seasonal patterns among the five cities. In Beijing and Xi’an, MDA8 O3 showed the highest in summer and lowest in winter. Guangzhou also had the highest O3 concentration in summer, but had similar levels in other three seasons. The O3 levels were similarly high in Shanghai during spring, summer, and autumn, while in Hefei, O3 concentration peaked in autumn. No significant difference between weekend and weekday O3 levels was observed in all the five cities. The diurnal cycle reached a maximum in the afternoon and a minimum in the early morning, which was consistent in the five cities. Correlation analyses showed that the associations between O3 and the other five criteria air pollutants, as well as meteorological parameters, were substantially different among the five cities. Air mass cluster analyses during episodic days revealed that the short-distance transport of O3 and its precursors had a greater impact for high O3 pollution in the five cities. Overall, our results demonstrate that O3 pollution exhibited great divergence among different regions and thus region-oriented control measures are suggested to reduce O3 pollution in China.
Collapse
|
33
|
Yin CQ, Solmon F, Deng XJ, Zou Y, Deng T, Wang N, Li F, Mai BR, Liu L. Geographical distribution of ozone seasonality over China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 689:625-633. [PMID: 31279208 DOI: 10.1016/j.scitotenv.2019.06.460] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Revised: 06/25/2019] [Accepted: 06/27/2019] [Indexed: 06/09/2023]
Abstract
Up to now, a nation-wide scale study of surface ozone (O3) concentrations in China was limited due to scarce observation. Thanks to the establishment of national air quality monitoring network in 2013, surface O3 data from 1402 stations during 2014-2017 were collected to investigate O3 seasonality. Our analysis reveals that the variations of monthly O3 averaged from daily mean concentration during a year show different temporal profiles depending on latitude. A unimodal structure (UMS) is generally found for latitudes over 35°N, whereas a bimodal structure (BMS) is in most of the cases identified south of 35°N. The peak of UMS is found in the period of May to July, whereas the first and second peaks of BMS are found from April to June, and from July to October, respectively. In addition, the seasonality of O3 presents a strong dependence on pseudo-equivalent potential temperature and monsoonal clouds. The onset and retreat of warm and wet air are correlated to the summer minimum in BMS cases and to the sharp decrease of UMS in July. As far as the relationships between O3 and carbon monoxide are concerned, the effects of clean maritime air masses on the summer trough of O3 are not significant for inland sites. Overall, summer monsoon bringing warm and moist air and subsequent clouds leads to the suppression of photochemical production, thereby contributing directly to the geographical distribution of O3 seasonality.
Collapse
Affiliation(s)
- C Q Yin
- Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou, China.
| | - F Solmon
- Laboratoire d'Aérologie, Centre National de la Recherche Scientifique, Toulouse, France
| | - X J Deng
- Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou, China
| | - Y Zou
- Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou, China
| | - T Deng
- Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou, China
| | - N Wang
- Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou, China
| | - F Li
- Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou, China
| | - B R Mai
- Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou, China
| | - L Liu
- Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou, China
| |
Collapse
|
34
|
Spatial and temporal variations of air quality and six air pollutants in China during 2015-2017. Sci Rep 2019; 9:15201. [PMID: 31645580 PMCID: PMC6811589 DOI: 10.1038/s41598-019-50655-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 08/06/2019] [Indexed: 12/05/2022] Open
Abstract
Air pollution has aroused significant public concern in China, therefore, long-term air-quality data with high temporal and spatial resolution are needed to understand the variations of air pollution in China. However, the yearly variations with high spatial resolution of air quality and six air pollutants are still unknown for China until now. Therefore, in this paper, we analyze the spatial and temporal variations of air quality and six air pollutants in 366 cities across mainland China during 2015–2017 for the first time to the best of our knowledge. The results indicate that the annual mean mass concentrations of PM2.5, PM10, SO2, and CO all decreased year by year during 2015–2017. However, the annual mean NO2 concentrations were almost unchanged, while the annual mean O3 concentrations increased year by year. Anthropogenic factors were mainly responsible for the variations of air quality. Further analysis suggested that PM2.5 and PM10 were the main factors influencing air quality, while NO2 played an important role in the formation of PM2.5 and O3. These findings can provide a theoretical basis for the formulation of future air-pollution control policy in China.
Collapse
|
35
|
Zhang Y, Wang S, Fan X, Xin J, Cheng Y. A temperature indicator for heavy air pollution risks (TIP). THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 678:712-720. [PMID: 31078862 DOI: 10.1016/j.scitotenv.2019.05.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 03/31/2019] [Accepted: 05/01/2019] [Indexed: 06/09/2023]
Abstract
This study presents a temperature indicator for heavy air pollution risks (TIP). This TIP indicator is a temperature threshold, at which the concentrations of air pollutants and air pollution indices easily reach their maxima. A connotation discussion ascertains that TIP is intrinsically associated with the atmospheric structure and static stability in the planetary boundary layer, especially during stagnant weather and moderately cold temperatures. Thus, TIP offers a simple measure of meteorological conditions that reflect the boundary layer static characteristics. It therefore provides a simple measure of air pollution potential, and can be used to quantify the potential air pollution severity for a given pollution emission level. From the data collected in China, TIP increases gradually from north to south and decreases slightly from east to west, which essentially indicates a function of latitude and altitude. A simple regressive function is built for TIP, and the resulting TIP index further confirms that it is indicative of the potential heavy air pollution events across the country. Being an easy-to-obtain and simple-to-use meteorological parameter, TIP can be used for air pollution potential assessment and prediction, as well as pollution prevention and control applications.
Collapse
Affiliation(s)
- Ying Zhang
- College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610000, Sichuan, China.
| | - Shigong Wang
- College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610000, Sichuan, China.
| | - Xingang Fan
- Department of Geography and Geology, Western Kentucky University, Bowling Green, KY 42101, USA; College of Electric Engineering, Chengdu University of Information Technology, Chengdu 610000, Sichuan, China
| | - Jinyuan Xin
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Yifan Cheng
- College of Atmospheric Science, Center for Meteorological Environment and Human Health, Lanzhou University, the Gansu key Laboratory of Arid Climate Change and Reducing Disaster, Lanzhou 730000, Gansu, China
| |
Collapse
|
36
|
The Spatial–Temporal Variation of Tropospheric NO2 over China during 2005 to 2018. ATMOSPHERE 2019. [DOI: 10.3390/atmos10080444] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In recent years, new and strict air quality regulations have been implemented in China. Therefore, it is of great significance to evaluate the current air pollution situation and effectiveness of actions. In this study, Ozone Monitoring Instrument (OMI) satellite data were used to detect the spatiotemporal characteristics of tropospheric NO2 columns over China from 2005 to 2018, including spatial distribution, seasonal cycles and long-term trends. The averaged NO2 pollution is higher in southeastern China and lower in the northwest, which are well delineated by the Heihe–Tengchong line. Furthermore, the NO2 loadings are highest in the North China Plain, with vertical column density (VCD) exceeding 13 × 1015 molec cm−2. Regarding the seasonal cycle, the NO2 loadings in eastern China is highest in winter and lowest in summer, while the western region shows the opposite feature. The amplitude of annual range increase gradually from the south to the north. If the entire period of 2005–2018 is taken into account, China has experienced little change in NO2. In fact, however, there appears to be significant trends of an increase followed by a downward tendency, with the turning point in the year 2012. In the former episode of 2005–2012, increasing trends overwhelm nearly the whole nation, especially in the Jing–Jin–Tang region, Shandong Province, and Northern Henan and Southern Hebei combined regions, where the rising rates were as high as 1.0–1.8 × 1015 molec cm−2 year−1. In contrast, the latter episode of 2013–2018 features remarkable declines in NO2 columns over China. Particularly, the regions where the decreased degree was remarkable in 2013–2018 were consistent with the regions where the upward trend was obvious in 2005–2012. Overall, this upward–downward pattern is true for most parts of China. However, some of the largest metropolises, such as Beijing, Shanghai and Guangzhou, witnessed a continuous decrease in the NO2 amounts, indicating earlier and more stringent measures adopted in these areas. Finally, it can be concluded that China’s recent efforts to cut NO2 pollution are successful, especially in mega cities.
Collapse
|
37
|
Lu H, Lyu X, Cheng H, Ling Z, Guo H. Overview on the spatial-temporal characteristics of the ozone formation regime in China. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2019; 21:916-929. [PMID: 31089656 DOI: 10.1039/c9em00098d] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Ozone (O3), a main component in photochemical smog, is a secondary pollutant formed through complex photochemical reactions involving nitrogen oxides (NOx) and volatile organic compounds (VOCs). In the past few decades, with the rapid economic development, industrialization and urbanization, the mixing ratio of O3 has increased substantially in China. O3 non-attainment days have been frequently observed. Despite great efforts made in the past few years, it is still difficult to alleviate O3 pollution in China, due to its non-linear relationship with the precursors. In view of the severe situation in China, this study presents a comprehensive review on the spatial-temporal variations of the relationship between O3 and its precursors (i.e. O3 formation regime), built upon the previous reviews of the spatial-temporal variations of O3 and its precursor levels. Valuable findings from previous studies are laid out for a better understanding of O3 pollution, followed by implications for the control of O3 pollution. This literature review indicates that O3 formation in most areas of the North China Plain (NCP), Yangtze River Delta (YRD) and Pearl River Delta (PRD) regions is in a VOC-limited regime during the high-O3 seasons due to dramatic emissions from human activities in cities. Outside these metropolitan areas, a NOx-limited regime dominates rural/remote areas. From summer to winter, the O3 formation regime over China shows a tendency to shift to a VOC-limited regime. Furthermore, O3 formation in China shifted toward increasing sensitivity to VOC emissions before the 12th Five-Year-Plan. However, after the 12th Five-Year-Plan, successful reduction of NOx slowed down this trend. Further effective control of VOCs is expected to achieve sustained O3 attainment in the future. To timely solve the current O3 pollution problem, precise control of O3 precursors is proposed, together with the joint prevention and control of regional air pollution.
Collapse
Affiliation(s)
- Haoxian Lu
- Air Quality Studies, Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
| | | | | | | | | |
Collapse
|
38
|
Zeng Y, Cao Y, Qiao X, Seyler BC, Tang Y. Air pollution reduction in China: Recent success but great challenge for the future. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 663:329-337. [PMID: 30711599 DOI: 10.1016/j.scitotenv.2019.01.262] [Citation(s) in RCA: 136] [Impact Index Per Article: 27.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 01/16/2019] [Accepted: 01/18/2019] [Indexed: 06/09/2023]
Abstract
China's rapid economic growth has caused severe air pollution, raising serious concerns about the growing evidence of its negative health, environmental, and economic impacts. Consequently, the Chinese government has implemented a number of policies and measures to reduce air pollution. Relying on published information over the last three decades in China, we analyzed trends in air pollutant emissions (SO2 and NOx) and concentrations of particulate matter (PM) and ozone (O3). During the past decade, SO2 and NOx emissions had declined throughout China and concentrations of PM2.5 and PM10 had considerably decreased in most cities, but average reported 90th MDA8 O3, M7, and AOT40 O3 for 31 capital cities showed an increasing trend between 2013 and 2017. Despite progress in air pollution reduction and an increasing number of "clear sky" days, PM concentrations throughout China remain higher than the World Health Organization guidelines, and urban smog and haze remain a major threat to human health and the environment. Thus far, significant emission reductions have occurred largely through robust administrative power, especially when emission reductions were tied to the performance evaluations and promotion of government officials. Similar to most already-industrialized nations, China is now shifting away from SO2-dominated to NOx- and O3-dominated air pollution. Existing technologies and improved operations of existing control equipment appear unlikely to achieve sufficient reductions in NOx and O3 pollution. Considering the complex relationship between O3, NOx, VOCs, weather, and socio-economic changes in China, it is necessary to increase research on impacts of increasing ozone on plants and to adopt novel technologies and implemented to further reduce air pollution to levels that will protect human health and the environment.
Collapse
Affiliation(s)
- Yingying Zeng
- Department of Environment, College of Architecture and Environment, Sichuan University, No. 24, South Section One, First Ring Road, Chengdu, Sichuan 610065, China
| | - Yuanfei Cao
- Institute of New Energy and Low-Carbon Technology, Sichuan University, No. 24, South Section One, First Ring Road, Chengdu, Sichuan 610065, China
| | - Xue Qiao
- Institute of New Energy and Low-Carbon Technology, Sichuan University, No. 24, South Section One, First Ring Road, Chengdu, Sichuan 610065, China; Healthy Food Evaluation Research Center, Sichuan University, No. 24, South Section One, First Ring Road, Chengdu, Sichuan 610065, China; State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, No. 24, South Section One, First Ring Road, Chengdu, Sichuan 610065, China
| | - Barnabas C Seyler
- Department of Environment, College of Architecture and Environment, Sichuan University, No. 24, South Section One, First Ring Road, Chengdu, Sichuan 610065, China
| | - Ya Tang
- Department of Environment, College of Architecture and Environment, Sichuan University, No. 24, South Section One, First Ring Road, Chengdu, Sichuan 610065, China; Healthy Food Evaluation Research Center, Sichuan University, No. 24, South Section One, First Ring Road, Chengdu, Sichuan 610065, China; State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, No. 24, South Section One, First Ring Road, Chengdu, Sichuan 610065, China.
| |
Collapse
|
39
|
Analysis of the Spatial–Temporal Variation of the Surface Ozone Concentration and Its Associated Meteorological Factors in Changchun. ENVIRONMENTS 2019. [DOI: 10.3390/environments6040046] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Ozone (O3) pollution has become one of the most challenging problems in China, and high O3 concentrations have been a major air quality issue in Changchun. Based on continuous observation data of surface ozone concentrations from ten automatic air monitoring stations and meteorological data from the meteorological bureau in Changchun, the temporal and spatial variations of the O3 concentration and its relationships with meteorological factors were analyzed by correlation analysis during the period of 2013–2017. The results showed the following: A single apex model of the annual mean O3 concentrations of the daily maximum 8 h average (MDA8) was found from the data for 2013 to 2017 in Changchun, with the highest MDA8 O3 concentrations in 2015 and a slight decline from then until 2017. The O3 concentrations in the suburban areas and the south of Changchun were higher than those downtown and north of the city. The seasonal variation of O3 concentrations was obvious, following the order summer > spring > autumn > winter, which was similar to the results of neighboring cities and provinces in Changchun. The days on which O3 concentrations exceeded the standard were concentrated in summer and spring, and the total number of ozone excess days was 91 days; the maximum number of ozone excess days was in 2015. The O3 concentration exceeded the standard in Changchun mainly in March–August, and its monthly mean value curve showed a bimodal type in which the highest values appeared in May and July, while the lowest values appeared in December. The diurnal pattern of ozone showed a single peak mode, and the peak value usually appeared at 14:00–16:00 while the minimum value appeared at 07:00–08:00. O3 concentrations in Changchun and the six selected pollutants CO, NO, NO2, NOx, PM10, and PM2.5 were negatively correlated. Higher temperature is a necessary synoptic condition for ozone pollution in Changchun: when the temperature rose, O3 concentrations increased significantly; further, O3 concentrations were negatively correlated with relative humidity and atmospheric pressure and were positively correlated with temperature and solar radiation. The O3 concentrations were highest when the wind scale approached 14~20 km/h and the wind direction was S. Combined with the research results in the surrounding areas of Changchun, it is indicated that there may be an ozone contribution from south of Changchun through long-range pollution transport and tropospheric subsidence.
Collapse
|
40
|
Wang P, Guo H, Hu J, Kota SH, Ying Q, Zhang H. Responses of PM 2.5 and O 3 concentrations to changes of meteorology and emissions in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 662:297-306. [PMID: 30690364 DOI: 10.1016/j.scitotenv.2019.01.227] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Revised: 01/18/2019] [Accepted: 01/21/2019] [Indexed: 05/21/2023]
Abstract
Tremendous efforts have been made to reduce the severe air pollution in China since 2013. However, the annual and peak fine particulate matter (PM2.5) concentrations during severe events in winter did not always reduce as expected. This is partially due to the inter-annual variation of meteorology, which affects the emission, transport, transformation, and deposition processes of air pollutants. In this study, the responses of PM2.5 and ozone (O3) concentrations to changes in emission and meteorology from 2013 to 2015 were investigated based on ambient measurements and the Community Multi-Scale Air Quality (CMAQ) model simulations with anthropogenic emissions. It is found that emission reductions in 2014 and 2015 effectively reduced PM2.5 concentrations by 23.9 and 43.5 μg/m3, respectively, but was partially counteracted by unfavorable meteorology. The negative effects from unfavorable meteorology were significant in extreme pollution events. For example, in December 2015, unfavorable meteorology caused a great increase (90 μg/m3) of PM2.5 in Beijing. Reduction of primary PM and gaseous precursors led to 13.4 and 16.5 ppb increase of O3-8 h daily concentrations in the summertime in 2014 and 2015 in comparison of 2013, which was likely caused by the increase of solar actinic flux due to PM reduction. In addition, reduction of nitrogen oxides (NOx) emissions in areas with negative NOx-O3 sensitivity could lead to an increase of O3 formation when the reduction of volatile organic compounds (VOCs) was not sufficient. This unintended enhanced O3 formation could also lead to higher O3 in downwind areas. This study emphasizes the role of meteorology in pollution control, validates the effectiveness of PM2.5 control measures in China, and highlights the importance of appropriate joint reduction of NOx and VOCs to simultaneously decrease O3 and PM2.5 for higher air quality.
Collapse
Affiliation(s)
- Pengfei Wang
- Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Hao Guo
- Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China.
| | - Sri Harsha Kota
- Department of Civil Engineering, Indian Institute of Technology Guwahati, 781039, India
| | - Qi Ying
- Zachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843, USA
| | - Hongliang Zhang
- Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USA.
| |
Collapse
|
41
|
Spatio-Temporal Characteristics of Tropospheric Ozone and Its Precursors in Guangxi, South China. ATMOSPHERE 2018. [DOI: 10.3390/atmos9090355] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The temporal and spatial distributions of tropospheric ozone and its precursors (NO2, CO, HCHO) are analyzed over Guangxi (GX) in South China. We used tropospheric column ozone (TCO) from the Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) onboard the Aura satellite (OMI/MLS), NO2 and HCHO from OMI and CO from the Measurements of Pollution in the Troposphere (MOPITT) instrument in the period 2005–2016. The TCO shows strong seasonality, with the highest value in spring and the lowest value observed in the monsoon season. The seasonal variation of HCHO is similar to that of TCO, while NO2 and CO show slightly different patterns with higher values in spring and winter compared to lower values in autumn and summer. The surface ozone, NO2 and CO observed by national air quality monitoring network sites are also compared with satellite-observed TCO, NO2 and CO, showing good agreement for NO2 and CO but a different seasonal pattern for ozone. Unlike TCO, surface ozone has the highest value in autumn and the lowest value in winter. To reveal the difference, the vertical profiles of ozone and CO from the measurement of ozone and water vapor by airbus in-service aircraft (MOZAIC) observations over South China are also examined. The seasonal averaged vertical profiles of ozone and CO show obvious enhancements at 2–6 km altitudes in spring. Furthermore, we investigate the dependence of TCO and surface ozone on meteorology and transport in detail along with the ECMWF reanalysis data, Tropical Rainfall Measuring Mission (TRMM) 3BV42 dataset, OMI ultraviolet index (UV index) dataset, MODIS Fire Radiative Power (FRP) and back trajectory. Our results show that the wind pattern at 800 hPa plays a significant role in determining the seasonality of TCO over GX, especially for the highest value in spring. Trajectory analysis, combined with MODIS FRP suggests that the air masses that passed through the biomass burning (BB) region of Southeast Asia (SEA) induced the enhancement of TCO and CO in the upper-middle troposphere in spring. However, the seasonal cycle of surface ozone is associated with wind patterns at 950 hPa, and the contribution of the photochemical effect is offset by the strong summer monsoon, which results in the maximum surface ozone concentration in post-monsoon September. The variations in the meteorological conditions at different levels and the influence of transport from SEA can account for the vertical distribution of ozone and CO. We conclude that the seasonal distribution of TCO results from the combined impact of meteorology and long-term transport.
Collapse
|
42
|
Pyrgou A, Hadjinicolaou P, Santamouris M. Enhanced near-surface ozone under heatwave conditions in a Mediterranean island. Sci Rep 2018; 8:9191. [PMID: 29907840 PMCID: PMC6003925 DOI: 10.1038/s41598-018-27590-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Accepted: 05/08/2018] [Indexed: 11/09/2022] Open
Abstract
Near-surface ozone is enhanced under particular chemical reactions and physical processes. This study showed the seasonal variation of near-surface ozone in Nicosia, Cyprus and focused in summers when the highest ozone levels were noted using a seven year hourly dataset from 2007 to 2014. The originality of this study is that it examines how ozone levels changed under heatwave conditions (defined as 4 consecutive days with daily maximum temperature over 39 °C) with emphasis on specific air quality and meteorological parameters with respect to non-heatwave summer conditions. The influencing parameters had a medium-strong positive correlation of ozone with temperature, UVA and UVB at daytime which increased by about 35% under heatwave conditions. The analysis of the wind pattern showed a small decrease of wind speed during heatwaves leading to stagnant weather conditions, but also revealed a steady diurnal cycle of wind speed reaching a peak at noon, when the highest ozone levels were noted. The negative correlation of NOx budget with ozone was further increased under heatwave conditions leading to steeper lows of ozone in the morning. In summary, this research encourages further analysis into the persistent weather conditions prevalent during HWs stimulating ozone formation for higher temperatures.
Collapse
Affiliation(s)
- Andri Pyrgou
- Energy, Environment and Water Research Center, The Cyprus Institute, P.O. Box 27456, Nicosia, 1645, Cyprus.
| | - Panos Hadjinicolaou
- Energy, Environment and Water Research Center, The Cyprus Institute, P.O. Box 27456, Nicosia, 1645, Cyprus
| | - Mat Santamouris
- The Anita Lawrence Chair in High Performance Architecture, School of Built Environment, University of New South Wales, Sydney, 2052, Australia
| |
Collapse
|
43
|
Li J, Chen X, Wang Z, Du H, Yang W, Sun Y, Hu B, Li J, Wang W, Wang T, Fu P, Huang H. Radiative and heterogeneous chemical effects of aerosols on ozone and inorganic aerosols over East Asia. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 622-623:1327-1342. [PMID: 29890599 DOI: 10.1016/j.scitotenv.2017.12.041] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 11/14/2017] [Accepted: 12/04/2017] [Indexed: 06/08/2023]
Abstract
Currently, many challenges are faced in simulating ozone(O3), sulfate(SO42-), and nitrate(NO3-) concentrations over East Asia, particularly the overestimation of surface O3 and NO3- concentrations and underestimation of the SO42- concentration during haze episodes. In this study, we examined the radiative and heterogeneous chemical effects of aerosols by incorporating recently reported mechanisms, including self-amplifying SO42- formation, dinitrogen pentoxide (N2O5) hydrolysis, and a heterogeneous reaction converting gaseous nitric acid (HNO3) to nitric oxide (NOx), into a Nested Air Quality Prediction Modeling System. Uptakes by aerosols can be computed through a simple parameterization that is dependent on the aerosol core and shell species, shell thickness, and amount of aerosol liquid water. In this study, a 1-year simulation was conducted for 2013. The updated model successfully reproduced the seasonal and daily observations of O3, fine particulate matter, SO42-, and NO3- concentrations in East Asia. Our results revealed that heterogeneous reactions reduced more surface O3 concentrations (10-20 ppbv) in the polluted regions of East China than did perturbations in photolysis frequencies from aerosols, effectively again improving the comparison between simulations and observations. Oxidation of SO2 by NO2 on wet aerosols significantly enhanced SO42- formation, with sulfate covering approximately ~30-60% of total sulfate concentrations in North China Plain during haze days in winter. The uptake of reactive nitrogen species on aerosols effectively reduced NO3- concentrations and successfully balanced the NOx/HNO3 chemistry in the models. We recommended that larger reductions of gaseous precursors should be considered in China to achieve the national air quality objective. The results show that surface O3 concentrations over East China will increase if the emission of aerosols is reduced without corresponding reductions in O3 precursors.
Collapse
Affiliation(s)
- Jie Li
- LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China.
| | - Xueshun Chen
- LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| | - Zifa Wang
- LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| | - Huiyun Du
- LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| | - Weiyi Yang
- LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| | - Yele Sun
- LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| | - Bo Hu
- LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| | - Jianjun Li
- China National Environmental Monitoring Center, Beijing, China
| | - Wei Wang
- China National Environmental Monitoring Center, Beijing, China
| | - Tao Wang
- China National Environmental Monitoring Center, Beijing, China
| | - Pingqing Fu
- LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| | - Huili Huang
- LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
44
|
Telesnicki MC, Martínez-Ghersa MA, Ghersa CM. Plant oxidative status under ozone pollution as predictor for aphid population growth: The case of Metopolophium dirhodum (Hemiptera: Aphididae) in Triticum aestivum (Poales: Poaceae). BIOCHEM SYST ECOL 2018. [DOI: 10.1016/j.bse.2018.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
45
|
Zhao H, Zheng Y, Li T, Wei L, Guan Q. Temporal and Spatial Variation in, and Population Exposure to, Summertime Ground-Level Ozone in Beijing. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:E628. [PMID: 29596366 PMCID: PMC5923670 DOI: 10.3390/ijerph15040628] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Revised: 03/15/2018] [Accepted: 03/26/2018] [Indexed: 11/29/2022]
Abstract
Ground-level ozone pollution in Beijing has been causing concern among the public due to the risks posed to human health. This study analyzed the temporal and spatial distribution of, and investigated population exposure to, ground-level ozone. We analyzed hourly ground-level ozone data from 35 ambient air quality monitoring sites, including urban, suburban, background, and traffic monitoring sites, during the summer in Beijing from 2014 to 2017. The results showed that the four-year mean ozone concentrations for urban, suburban, background, and traffic monitoring sites were 95.1, 99.8, 95.9, and 74.2 μg/m³, respectively. A total of 44, 43, 45, and 43 days exceeded the Chinese National Ambient Air Quality Standards (NAAQS) threshold for ground-level ozone in 2014, 2015, 2016, and 2017, respectively. The mean ozone concentration was higher in suburban sites than in urban sites, and the traffic monitoring sites had the lowest concentration. The diurnal variation in ground-level ozone concentration at the four types of monitoring sites displayed a single-peak curve. The peak and valley values occurred at 3:00-4:00 p.m. and 7:00 a.m., respectively. Spatially, ground-level ozone concentrations decreased in gradient from the north to the south. Population exposure levels were calculated based on ground-level ozone concentrations and population data. Approximately 50.38%, 44.85%, and 48.49% of the total population of Beijing were exposed to ground-level ozone concentrations exceeding the Chinese NAAQS threshold in 2014, 2015, and 2016, respectively.
Collapse
Affiliation(s)
- Hui Zhao
- Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Youfei Zheng
- Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China.
- Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Ting Li
- Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Li Wei
- Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Qing Guan
- Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| |
Collapse
|