Spatial and temporal characteristics of air quality and air pollutants in 2013 in Beijing

Air quality characteristics during 2016-2020 in Wuhan, China

Implementation of a clean air policy in China has high national importance. Here, we analyzed tempo-spatial characteristics of the concentrations of PM_2.5 (PM_2.5_C), PM₁₀ (PM₁₀_C), SO₂ (SO₂ _C), NO₂ (NO₂ _C), CO (CO _C), and maximum 8-h average O₃ (O₃_8h_C), monitored at 22 stations throughout the mega-city of Wuhan from January 2016 to December 2020, and their correlations with the meteorological and socio-economic factors. PM_2.5_C, PM₁₀_C, SO₂ _C, NO₂ _C, and CO _C showed similar monthly and seasonal trends, with minimum value in summer and maximum value in winter. However, O_{3_}8h_C showed an opposite monthly and seasonal change pattern. In 2020, compared to the other years, the annual average PM_2.5_C, PM₁₀_C, SO₂ _C, NO₂ _C, and CO _C were lower. PM_2.5_C and PM₁₀_C were higher in urban and industrial sites and lower in the control site. The SO₂_C was higher in industrial sites. The NO₂_C was lower, and O_{3_}8h_C was higher in suburban sites, while CO showed no spatial differences in their concentrations. PM_2.5 _C, PM₁₀ _C, SO₂ _C, NO₂ _C, and CO _C had positive correlations with each other, while O_{3_}8h_C showed more complex correlations with the other pollutants. PM_2.5_C, PM₁₀_C, SO₂ _C, and CO _C presented a significantly negative association with temperature and precipitation, while O₃ was significantly positively associated with temperature and negatively associated with relative air humidity. There was no significant correlation between air pollutants and wind speed. Gross domestic product, population, number of automobiles, and energy consumption play an important role in the dynamics of air quality concentrations. These all provided important information for the decision and policy-makers to effectively control the air pollution in Wuhan.

Trait-mediated leaf retention of atmospheric particulate matter in fourteen tree species in southern China

Particulate air pollution is a serious threat to human health, especially in urban areas, and trees can act as biological filters and improve air quality. However, studies on greening tree species selection are rare. We measured three particular matter adsorption metrics (PM_2.5, PM_2.5-10, and PM_>10 captured per leaf area) and six functional traits for each of fourteen species and estimated their minimum light requirements based on field surveys. We found that shade-tolerant species captured more coarse particles (PM_2.5-10) than light-demanding species. For traits, a strong negative correlation was found between photosynthetic capacity and adsorption capacity for all three PM size fractions, indicating that in comparison to acquisitive species, conservative species captured larger amounts of particles. Moreover, denser wood species and smaller leaves were more efficient in capturing large particles (PM_>10), while species with "expensive" leaves (high leaf N or P) were more efficient in capturing fine particles (PM_2.5), indicating that capturing large and fine particles was related to mechanical stability traits and leaf surface traits, respectively. Our results demonstrated that the metabolism (e.g., photosynthetic capacity) and chemistry (e.g., leaf N and leaf P) of leaves help explain species capacity to capture PM. We encourage future studies to investigate the ecosystem functions and stress tolerance of tree species with the same framework and trait-based methods.

Genetic damage and potential mechanism exploration under different air pollution patterns by multi-omics

Ambient air pollution was classified as carcinogenic to humans (Group 1) for lung cancer. DNA damage was an important first step in the process of carcinogenesis, and could also be induced by air pollution. In this study, intratracheal instillation and real-time air exposure system were combined to establish SHP (short-term high-level PM_2.5) and LLPO (long-term low-level PM_2.5 and O₃) exposure patterns, respectively. Hierarchical levels of genetic biomarkers were analyzed to explore DNA damage effects in rats. Representative DNA repair genes from different repair pathways were selected to explore the relative expression levels. The methylation level of differentially expressed repair genes were also determined. Besides, miRNA sequencing and non-targeted metabolomic analysis were performed in rat lungs. KEGG and multi-omics analysis were used to explore the potential mechanism of genetic damage under different air pollution patterns. We found that LLPO exposure induced DSBs and chromosome damage. SHP exposure could induce DSBs and DNA oxidative damage, and the effects of genetic damage under this pollution pattern could be repaired by natural repair. Repair genes involved in two pattern were different. SHP exposure could induce higher methylation levels of RAD51, which might be a potential epigenetic mechanism for high-level PM_2.5 induced down-regulated expression of RAD51 and DSBs. Besides, 29 overlapped alterations in metabolic pathways were identified by metabolomic and miRNA sequencing, including purine metabolism and pyrimidine metabolism after LLPO exposure. Differential miRNAs expression in lung tissue were associated with apoptosis, DNA damage and damage repair. We concluded that under different air pollution patterns, DNA damage biomarkers and activated targets of DNA damage repair network were both different. The genetic damage effects caused by high-level short-term PM_2.5 can be alleviated by natural repair. We provided possible mechanisms by multi-omics which could explain the increased carcinogenic risk caused by air pollution.

Characterization of Ambient Air Quality in Selected Urban Areas in Uganda Using Low-Cost Sensing and Measurement Technologies

Air pollution is prevalent in cities and urban centers in developing countries including sub-Saharan Africa, but ground monitoring data on local pollution remain inadequate, hindering effective mitigation. We employed low-cost sensing and measurement technologies to quantify pollution levels based on particulate matter (PM_2.5), NO₂, and O₃ over a 6 month period for selected urban centers in three of the four macroregions in Uganda. PM_2.5 diurnal profiles exhibited consistent patterns across all monitoring locations with higher pollution levels manifesting from 18:00 to 00:00 and from 06:00 to 09:00; while the periods from 00:00 to 05:00 and from 09:00 to 17:00 had the lowest levels. Daily PM_2.5 varied widely between 34 and 107 μg/m³ over a 7 day period, well within unhealthy levels (55.5-150.4 μg/m³) for short-term exposure. The inconsistent daily trend are instructive for multiple pollutant assessment to aid specific policy initiatives. The results also show inverse relations between seasonal particulate levels and precipitation, that is, R (correlation coefficient) = -0.93 and -0.62 for Kampala and Wakiso, R = -0.49 and -0.44 for the Eastern region, and R = -0.65 and -0.96 for the Western region. NO₂ monthly concentrations replicated PM_2.5 spatial levels, whereas O₃ exhibited inverse relations probably due to a higher retention time in less-urbanized environments. Both PM_2.5 and NO₂ correlated positively with the resident population. Our findings show significant spatiotemporal variations and exceedances of health guidelines by about 4-6 times across most study locations (with two exceptions) for longer-term exposure. This paper demonstrably highlights the practicability and potential of low-cost approaches for air quality monitoring, with strong prospects for citizen science. This paper also provides novel information regarding air pollution that is needed to improve control strategies for reducing exposures.

Assessment of temporal shifting of PM2.5, lockdown effect, and influences of seasonal meteorological factors over the fastest-growing megacity, Dhaka

Dhaka is subjected to high pollution levels throughout the year, holding some relatively high amounts of pollution readings, making its air unhealthy to breathe. The study examined hourly, shifting, seasonal fluctuations in particulate matter (PM_2.5), the effects of seasonal meteorological variables, and the lockdown effect over the megacity of Dhaka from 2019 to 2021 using data from AirNow. The results indicate the daily average PM_2.5 concentration between 2019 and 2021 was 112.49 µg/m³, about four times higher than the WHO limit and two times higher than the Bangladesh standard. Daily PM_2.5 concentrations was high during morning and evening pick-up hours, reaching a maximum hourly concentration of 472.9 µg/m³ in February 2020. The maximum average PM_2.5 concentration was 211.23 µg/m³ in March 2021 (winter season), and the lowest average was 27.58 µg/m³ in August 2020 (rainy season). The Pearson correlation coefficient (r) between the PM_2.5 and meteorological variables were inverse with rainfall (− 0.62), temperature (− 0.73), humidity (− 0.82), but positive with wind (0.09). Daily average Air Quality Index (AQI) concentrations improved from 108.53 to 67.99 µg/m³ during the lockdown period. Finally, the study recommended many mitigation strategies that might assist accountable authorities in lowering the number of life-threatening components in the air.

Air quality during three covid-19 lockdown phases: AQI, PM2.5 and NO2 assessment in cities with more than 1 million inhabitants

Implemented quarantine due to the ongoing novel coronavirus (agent of COVID-19) has an immense impact on human mobility and economic activities as well as on air quality. Since then, and due to the drastic reduction in pollution levels in cities across the world, a large discussion has been magnetized regarding if the lockdown is an adequate alternative counter-measure for enhancing air quality. This paper aimed at studying the Air Quality Index (AQI), PM2.5, and tropospheric NO2 levels in three lockdown phases (before, during, and after) among 21 cities around the world. Simple before/after comparison approach was carried out to capture the declining trend in air pollution levels caused by the lockdown restrictions. The results showed that the frequency distribution for NO2 is more variable than that for PM2.5, and the distribution is flatter from 2020 to the baseline 2018-2019 period. Besides, AQI, in most of the cities, has varied from high to mild pollution during the lockdown and was moderate before. Although during the lockdown, a reduction of 3 to 58% of daily NO2 concentrations was observed across the cities, an increase was detected in three cities including Abidjan (1%), Conakry (3%), and Chengdu (10%). Despite this mixed trend, the NO2 time series clearly showed the effect of the unlocking phase where the NO2 levels increased in almost all cities. Similarly, PM2.5 concentrations have increased in the post-lockdown period, with 50% of the cities reporting significant positive differences between the lock and the unlock phase. Then, the levels of PM2.5 were higher at the pre-lockdown phase than at any other time exhibiting a "U" shape. In addition, during Ramadan, it was noted that altered patterns of daily activities in some Islamic cities have a significant negative impact on air quality.

Relationship between Air Pollutant Exposure and Gynecologic Cancer Risk

Exposure to air pollution has been suggested to be associated with an increased risk of women's health disorders. However, it remains unknown to what extent changes in ambient air pollution affect gynecological cancer. In our case-control study, the logistic regression model was combined with the restricted cubic spline to examine the association of short-term exposure to air pollution with gynecological cancer events using the clinical data of 35,989 women in Beijing from December 2008 to December 2017. We assessed the women's exposure to air pollutants using the monitor located nearest to each woman's residence and working places, adjusting for age, occupation, ambient temperature, and ambient humidity. The adjusted odds ratios (ORs) were examined to evaluate gynecologic cancer risk in six time windows (Phase 1-Phase 6) of women's exposure to air pollutants (PM2.5, CO, O3, and SO2) and the highest ORs were found in Phase 4 (240 days). Then, the higher adjusted ORs were found associated with the increased concentrations of each pollutant (PM2.5, CO, O3, and SO2) in Phase 4. For instance, the adjusted OR of gynecological cancer risk for a 1.0-mg m-3 increase in CO exposures was 1.010 (95% CI: 0.881-1.139) below 0.8 mg m-3, 1.032 (95% CI: 0.871-1.194) at 0.8-1.0 mg m-3, 1.059 (95% CI: 0.973-1.145) at 1.0-1.4 mg m-3, and 1.120 (95% CI: 0.993-1.246) above 1.4 mg m-3. The ORs calculated in different air pollution levels accessed us to identify the nonlinear association between women's exposure to air pollutants (PM2.5, CO, O3, and SO2) and the gynecological cancer risk. This study supports that the gynecologic risks associated with air pollution should be considered in improved public health preventive measures and policymaking to minimize the dangerous effects of air pollution.

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

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 NO_x may increase O₃ concentration. When the emission reduction ratio of anthropogenic VOCs/NO_x 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.

Urban Air Pollution Mapping Using Fleet Vehicles as Mobile Monitors and Machine Learning

Spatially explicit urban air quality information is important for developing effective air quality control measures. Traditionally, urban air quality is measured by networks of stationary monitors that are not universally available and sparsely sited. Mobile air quality monitoring using equipped vehicles is a promising alternative but has focused on vehicle-level experiments and lacks fleet-level demonstration. Here, we equipped 260 electric vehicles in a ride-hailing fleet in Beijing, China with low-cost sensors to collect real-time, spatial-resolved data on fine particulate matter (PM_2.5) concentrations. Using this data, we developed a decision tree model to infer the distribution of PM_2.5 concentrations in Beijing at 1 km by 1 km and 1 h resolution. Our results are able to show both short- and long-term variations of urban PM_2.5 concentrations and identify local air pollution hotspots. Compared with a benchmark model that only uses data from stationary monitoring sits, our model has shown significant improvement with the coefficient of determination increased from 0.56 to 0.80 and root mean square error decreased from 12.6 to 8.1 μg/m³. To the best of our knowledge, this study collects the largest mobile sensor data for urban air quality monitoring, which are augmented by state-of-the-art machine learning techniques to derive high-quality urban air pollution mapping. Our results demonstrate the potential and necessity of using fleet vehicles as routine mobile sensors combined with advanced data science methods to provide high-resolution urban air quality monitoring.

Quantifying the Deposition of Airborne Particulate Matter Pollution on Skin Using Elemental Markers

Airborne particulate matter (PM) pollution is an environmental and health concern. The health impact of PM pollution has typically focused on the respiratory system. The impact of PM pollution on skin has been largely understudied due to the lack of a quantitative method to measure the deposition on skin. This manuscript presents a method to quantify PM pollution on skin using elemental markers as a proxy for PM. Skin tape strips were collected from forehead and buttock of 100 outdoor workers in Beijing, China. Skin samples were analyzed for 19 elemental markers using inductively coupled plasma mass spectrometry. To determine the specific elemental signature of PM for the region, air samples were collected over 7 days for PM < 2.5 μm (PM_2.5) and analyzed for the same 19 elements as the skin samples. An enrichment factor was calculated for each element and the potential source was evaluated. Using the elemental markers unique to PM pollution for the region, the PM concentration deposited on skin was determined to be 0.621-2.53 μg PM_2.5 /cm². This method can be re-applied in different regions and the PM concentration on skin can inform future studies on the health impact of air pollution on skin.

Seasonal variation of epiphytic bacteria in the phyllosphere of Gingko biloba, Pinus bungeana and Sabina chinensis

Phyllosphere harbors diverse microorganisms, which influence plant growth and health. In order to understand the extent to which environmental factors affect epiphytic microbial communities, we characterized microbial communities on leaves of three separate tree species present on the college campus, and also present within a forest park over two seasons. Quantitative PCR analysis showed the quantity of 16S rRNA genes was lower in May compared with October, while the abundances of functional genes (nifH and bacterial amoA genes) were extremely high in May. High-throughput sequencing revealed a large variation in the diversity and composition of bacterial and diazotrophic communities over the two seasons, and showed the abundance of functional genera, such as Nocardioides, Bacillus and Zoogloea were significantly elevated in May. In addition, xenobiotic biodegradation pathways of bacterial communities were clearly elevated in May. Network analysis showed the correlations between phyllospheric bacteria in May were more complex than that in October and showed greater negative correlations. These results were consistent in all tree species in this study. This study showed that phyllospheric bacteria varied greatly in different seasons, which implies that different growing seasons should be considered in the exploitation of the interactions between phyllospheric microorganisms and host plants.

COVID-19 lockdown: a boon in boosting the air quality of major Indian Metropolitan Cities

ABSTRACT

The COVID-19 lockdown has not only helped in combating the community transmission of SARS-CoV-2 but also improved air quality in a very emphatic manner in most of the countries. In India, the first phase of COVID-19 lockdown came into force on March 25, 2020, which was later continued in the next phases. The purpose of this study was to investigate the result of lockdown on air quality of major metropolitan cities-Delhi, Mumbai, Kolkata, Chennai, Bengaluru, Hyderabad, Jaipur, and Lucknow-from March 25 to May 3, 2020. For this study, the concentration of six criteria air pollutants (PM_2.5, PM₁₀, CO, NO₂, SO₂, and O₃) and air quality index during the COVID-19 lockdown period was compared with the same period of the previous year 2019. The results indicate a substantial improvement in air quality with a drastic decrease in the concentration of PM_2.5, PM₁₀, CO, and NO₂, while there is a moderate reduction in SO₂ and O₃ concentration. During the lockdown period, the maximum reduction in the concentration of PM_2.5, PM₁₀, CO, NO₂, SO₂, and O₃ was observed to be - 49% (Lucknow), - 57% (Delhi), - 75% (Mumbai), - 68% (Kolkata), - 48% (Mumbai), and - 29% (Hyderabad), respectively. The value of the air quality index (AQI) also dwindled significantly during the COVID-19 lockdown period. The maximum decline in AQI was observed - 52% in Bengaluru and Lucknow. The order of AQI was satisfactory > moderate > good > poor and the frequency order of prominent pollutants was O₃ > PM₁₀ > PM_2.5 > CO > NO₂ > SO₂ during the lockdown period in all the aforementioned metropolitan cities.

Bacterial composition and community structure of the oropharynx of adults with asthma are associated with environmental factors

The development and exacerbation of asthma are mainly attributed to inflammatory reactions caused by allergens. However, less is known about the development of asthma caused by microbial disorders in the oropharynx and induced by environmental factors. Here, the metagenome of the oropharyngeal microbiome of adults with asthma was analysed to identify their association with air pollutants. Oropharyngeal swabs from patients with asthma were collected in two winters (W1 and W2) with different environmental factor exposures. The bacterial composition and community structure of the oropharynx were analysed through high-throughput sequencing. After analysis, the α-diversity and β-diversity exhibited significant differences between the two groups. LEfSe analysis detected 8 significantly different phyla and 11 significantly different genera between the W1 and W2 groups. Multiple linear regression analyses found that the asthma status might contribute to the alteration of microbial composition. Redundancy analysis showed that NO₂ was the only environmental factor that significantly affected the microbial community structure of the oropharynx. The different genera associated with NO₂ were Rothia, Actinomyces, Fusobacterium and Leptotrichia. The altered taxa related to PM_2.5 were Cupriavidus and Acinetobacter. Actinobacillus and Prevotella showed a highly positive correlation with O₃. Moreover, network analysis was carried out to explore the co-occurrence relationships of the main genera, and PICRUSt was conducted to predict bacterial functions. This study showed that environmental factors might cause alteration in the oropharyngeal flora, which might be a potential risk factor of asthma.

Response of the particulate matter capture ability to leaf age and pollution intensity

Differences in leaf surface microstructure characteristics can lead to differences in the ability of trees to capture suspended particulate matter (PM). The influence of changes in leaf surface microstructure caused by growth and environmental pollution on the PM capture ability is poorly understood. This study assessed the influence of growth on leaf microstructure in leaves of different ages, and the influence of pollution intensity was assessed by studying trees growing under different pollution conditions. It was found that the ability of leaves of Taxus cuspidata var., Platycladus orientalis, and Pinus tabuliformis to absorb total suspended particles (TSP), PM10, PM2.5, and PM1 increased with leaf age. The amounts of TSP and PM10 captured by P. orientalis, P. tabuliformis, Sophora japonica, Populus tomentosa, and Ginkgo biloba were higher in heavily polluted areas than in clean areas. This may be because particle capture is influenced by leaf microstructure changes. With age increasing, the root mean square roughness (Rq) of three evergreen species leaves increased. Environmental pollution will change the leaf surface microstructure and its ability to capture PM. Compared with a clean area, in a heavily polluted area, the stomatal index of the leaves decreased, stomata were occluded, the leaf wax layer was degraded, the leaf surface contained more particles, the surface texture of S. japonica and G. biloba leaves became irregular, the boundaries of the epidermal cells became more irregular, and the trichrome of S. japonica became thinner, longer, and harder. The Rq value was generally higher in the heavily polluted area, and the roughness of the abaxial surface increased more than on the adaxial surface. In the heavily polluted area, the leaf microstructure changes were the main reason for the increase in the Rq value. With the increase in leaf roughness, the amount of PM on the leaf surface increased.

Effects of Meteorological Factors and Anthropogenic Precursors on PM2.5 Concentrations in Cities in China

Fine particulate matter smaller than 2.5 μm (PM2.5) in size can significantly affect human health, atmospheric visibility, climate, and ecosystems. PM2.5 has become the major air pollutant in most cities of China. However, influencing factors and their interactive effects on PM2.5 concentrations remain unclear. This study used a geographic detector method to quantify the effects of anthropogenic precursors (AP) and meteorological factors on PM2.5 concentrations in cities of China. Results showed that impacts of meteorological conditions and AP on PM2.5 have significant spatio-temporal disparities. Temperature was the main influencing factor throughout the whole year, which can explain 27% of PM2.5 concentrations. Precipitation and temperature were primary impacting factors in southern and northern China, respectively, at the annual time scale. In winter, AP had stronger impacts on PM2.5 in northern China than in other seasons. Ammonia had stronger impacts on PM2.5 than other anthropogenic precursors in winter. The interaction between all factors enhanced the formation of PM2.5 concentrations. The interaction between ammonia and temperature had strongest impacts at the national scale, explaining 46% (q = 0.46) of PM2.5 concentrations. The findings comprehensively elucidated the relative importance of driving factors in PM2.5 formation, which can provide basic foundations for understanding the meteorological and anthropogenic influences on the concentration patterns of PM2.5.

Spatial and temporal variations of air quality and six air pollutants in China during 2015-2017

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.

Field assessment of the effects of land-cover type and pattern on PM10 and PM2.5 concentrations in a microscale environment

The microscale environment is a very important human-scale outdoor spatial unit. Aimed at investigating the effects of microscale land-cover type and pattern on levels of PM₁₀ and PM_2.5, we monitored PM₁₀ and PM_2.5 concentrations among different land-cover type and pattern sites through field measurements, during four seasons (December 2015 to November 2016) in Beijing, China. Differences of daily PM₁₀ and PM_2.5 concentrations among seven typical land-cover types, and correlations between daily two-sized PM levels and various microscale land-cover patterns as explained by landscape metrics were analyzed. Results show that concentrations of the two-sized particles had stable daytime and seasonal trends. During the four seasons, there were various differences in daily PM₁₀ and PM_2.5 levels among the seven land-cover types. Overall, bare soil always had the highest daily PM₁₀ level, whereas high canopy density vegetation and water bodies had low levels. Maximum PM_2.5 levels were always found in high canopy density vegetation. Moderate canopy density vegetation and water bodies had lower concentrations. Correlations between different landscape metrics and daily levels of two-sized PM varied by season. Metrics reflecting the dominance and distribution of land-cover classifications had closer relationships with particle concentrations in the microscale environment. The patterns of pavement along with low and moderate canopy density vegetation had a greater impact on PM₁₀ level. The responses of PM_2.5 level to patterns of building and low and moderate canopy density vegetation were sensitive. Reasonable design of land-cover structure would be conducive to ameliorate air particle concentrations in the microscale environment. Graphical abstract ᅟ.

Spatial and Temporal Variations of Six Criteria Air Pollutants in Fujian Province, China

Air pollution has become a critical issue in the urban areas of southeastern China in recent years. A complete understanding of the tempo-spatial characteristics of air pollution can help the public and governmental bodies manage their lives and work better. In this study, data for six criteria air pollutants (including particulate matter (PM_2.5, PM₁₀), carbon monoxide (CO), sulfur dioxide (SO₂), nitrogen dioxide (NO₂) and ozone (O₃)) from 37 sites in nine major cities within Fujian Province, China were collected between January 2015 to December 2016, and analyzed. We analyzed the spatial and temporal variations of these six criteria pollutants, as well as the attainment rates, and identified what were the major pollutants. Our results show that: (1) the two-year mean values of PM_2.5 and PM₁₀ exceeded the Chinese National Ambient Air Quality Standard (CAAQS) standard I levels, whereas other air pollutants were below the CAAQS standard I; (2) the six criteria air pollutants show spatial variations (i.e. most air pollutants were higher in the city center areas, followed by suburban areas and exurban areas, except for O₃; and the concentrations of PM₁₀, PM_2.5, NO₂, O₃ were higher in coastal cities than in inland cities); (3) seasonal variations and the no attainment rates of air pollutants were found to be higher in cold seasons and lower in warm seasons, except for O₃; (4) the most frequently present air pollutant was PM₁₀, with PM_2.5 and O₃ being the second and third most frequent, respectively; (5) all the air pollutants, except O₃, showed positive correlations with each other. These results provide additional information for the effective control of air pollution in the province of Fujian.

A factor analysis of landscape metrics of particles deposited on leaf surface

Particulate matter in the airborne environment is one of the top environmental concerns, as well as reasons of deaths and diverse diseases. Urban green infrastructure can improve the air quality by mitigating particulate matters from airborne environment and provide high spatial monitoring of particles by means of leaf particles as indicators. Three common species in Beijing (ailanthus, ash, and willow) were chosen to represent three different leaf characteristics. Then, we analyzed the correlation relationship of the particle metrics at landscape, class, and patch levels and implemented the principal components analysis and factor analysis. Firstly, at landscape level, metrics are mostly correlated with each other and the correlation relationship of metrics of ailanthus and willow were stronger than that of ash, which has coarse-texture leaves without hair. At class level, most of the metrics were correlated and the correlation relationship of metrics of ailanthus, whose leaves have microgrooves without hair, was weaker than that of ash and willow. At patch level, judging from proximity, the distance between particles from the same range was smaller for particles with complicated shape. Secondly, particles from four ranges were analyzed separately. The shape complexity of particles decreased and increased as the area increased respectively for PM1 (diameter ≤ 1 μm) and large particles (diameter ≥ 10 μm). Two principle components were identified for landscape and class levels respectively. These results will be useful for the in-depth understanding of the particles deposited on the leaf surface.

Analysis of Spatial-Temporal Characteristics of the PM2.5 Concentrations in Weifang City, China

Air pollution, which accompanies industrial progression and urbanization, has become an urgent issue to address in contemporary society. As a result, our understanding and continued study of the spatial-temporal characteristics of a major pollutant, defined as 2.5-micron or less particulate matter (PM2.5), as well as the development of related approaches to improve the environment, has become vital. This paper studies the characteristics of yearly, quarterly, monthly, daily, and hourly PM2.5 concentrations, and discusses the influencing factors based on the hourly data of nationally controlled and provincially controlled monitoring stations, from 2012 to 2016, in Weifang City. The main conclusion of this study is that the annual PM2.5 concentrations reached a peak in 2013. With efficient aid from the government, this value has decreased annually and has high spatial characteristics in the northwest and low spatial characteristics in the southeast. Second, the seasonal and monthly PM2.5 concentrations form a U-shaped trend, meaning that the concentration is high in the summer and low in the winter. These trends are highly relevant to the factors of plantation, humidity, temperature, and precipitation. Third, within a week, higher PM2.5 concentrations appear on Mondays and Saturdays, whereas the lowest concentration occurs on Wednesdays. It can be inferred that PM2.5 concentrations tend to be highly dependent on human activities and living habits. Lastly, there are hourly discrepancies within the peaks and troughs depending on the month, and the overall daytime PM2.5 concentrations and reductive rates are higher in the daytime than in the nighttime.

Long-term trends and health impact of PM2.5 and O3 in Tehran, Iran, 2006-2015

The main objectives of this study were (1) investigation of the temporal variations of ambient fine particulate matter (PM_2.5) and ground level ozone (O₃) concentrations in Tehran megacity, the capital and most populous city in Iran, over a 10-year period from 2006 to 2015, and (2) estimation of their long-term health effects including all-cause and cause-specific mortality. For the first goal, the data of PM_2.5 and O₃ concentrations, measured at 21 regulatory monitoring network stations in Tehran, were obtained and the temporal trends were investigated. The health impact assessment of PM_2.5 and O₃ was performed using the World Health Organization (WHO) AirQ+ software updated in 2016 by WHO European Centre for Environment and Health. Local baseline incidences in Tehran level were used to better reveal the health effects associated with PM_2.5 and O₃. Our study showed that over 2006-2015, annual mean concentrations of PM_2.5 and O₃ varied from 24.7 to 38.8 μg m^-3 and 35.4 to 76.0 μg m^-3, respectively, and were significantly declining in the recent 6 years (2010-2015) for PM_2.5 and 8 years (2008-2015) for O₃. However, Tehran citizens were exposed to concentrations of annual PM_2.5 exceeding the WHO air quality guideline (WHO AQG) (10 μg m^-3), U.S. EPA and Iranian standard levels (12 μg m^-3) during entire study period. We estimated that long-term exposure to ambient PM_2.5 contributed to between 24.5% and 36.2% of mortality from cerebrovascular disease (stroke), 19.8% and 24.1% from ischemic heart disease (IHD), 13.6% and 19.2% from lung cancer (LC), 10.7% and 15.3% from chronic obstructive pulmonary disease (COPD), 15.0% and 25.2% from acute lower respiratory infection (ALRI), and 7.6% and 11.3% from all-cause annual mortality in the time period. We further estimated that deaths from IHD accounted for most of mortality attributable to long-term exposure to PM_2.5. The years of life lost (YLL) attributable to PM_2.5 was estimated to vary from 67,970 to 106,706 during the study period. In addition, long-term exposure to O₃ was estimated to be responsible for 0.9% to 2.3% of mortality from respiratory diseases. Overall, long-term exposure to ambient PM_2.5 and O₃ contributed substantially to mortality in Tehran megacity. Air pollution is a modifiable risk factor. Appropriate sustainable control policies are recommended to protect public health.

The impacts of regional transport and meteorological factors on aerosol optical depth over Beijing, 1980-2014

Understanding the role of different sources that contribute to the aerosol extinction coefficient is an important aspect toward analyzing climate change and regional air quality. In Beijing specifically, the region has suffered severe air quality deterioration over the past three decades, but the magnitude of extraneous contributions to aerosol variation has remained uncertain. Therefore, we estimated trends of contributions to aerosol optical depth (AOD) for Beijing from 1980 to 2014 and built a seasonal regression model to decouple the extraneous contribution from the total emitted using ground-based aerosol and meteorological measurements, extended to the emissions of man-made and natural contribution. The variation of AOD over Beijing was significantly affected by the anthropogenic aerosol emissions, which experienced slight augmentation by 15.3% from 1980 to 2000, rapid inflation by 36.9% from 2000 to 2006, and a gradual decrease by 10.0% from 2006 to 2014. The extraneous contribution from wind and its associated languishing patterns explain the historical increase of regional AOD, which experienced about a 10% enhancement over the three stages. Other meteorological contributions show no significant trends over 35 years, except for the temperature inversion, which despite the weakened hygroscopic growth after 2006, still experiences a significant enhancement.

Spatio-temporal characteristics of urban air pollutions and their causal relationships: Evidence from Beijing and its neighboring cities

China has been suffering from serious air pollution for years in response to the rapid industrialization and urbanization. Notably Beijing is one of the most polluted capitals in the world. Hence, the focus of the study area is on Beijing. In the first stage, we analyze spatial and temporal characteristics of air pollution of the 6 cities while in the second stage the Granger causality test is applied to investigate whether air pollution of a city is affected by its neighbors, and vice versa. The findings are the following. Overall, AQI values are high in winter and early spring while low in summer and autumn. Among the 6 cities, Baoding is the major contributor to air pollution in this entire area. Besides, Granger causality test results show that there is a unidirectional relationship running from Baoding to Beijing and a bidirectional relationship between Beijing and Tianjin. In other words, apart from local air pollutants, for example, exhaust gas, air quality of Beijing is affected by air pollution of Tianjin, and vice versa. However, regarding the relationship between Beijing and Baoding, air quality of Beijing is just affected by air pollution of Baoding, since Baoding is much polluted than Beijing.

Chemical characterization and sources of PM2.5 at 12-hr resolution in Guiyang, China

The increasing emission of primary and gaseous precursors of secondarily formed atmospheric particulate matter due to continuing industrial development and urbanization are leading to an increased public awareness of environmental issues and human health risks in China. As part of a pilot study, 12-hr integrated fine fraction particulate matter (PM2.5) filter samples were collected to chemically characterize and investigate the sources of ambient particulate matter in Guiyang City, Guizhou Province, southwestern China. Results showed that the 12-hr integrated PM2.5 concentrations exhibited a daytime average of 51 ± 22μg·m-3 (mean ± standard deviation) with a range of 17-128μg·m-3 and a nighttime average of 55 ± 32μg·m-33 with a range of 4-186 μg·m-3. The 24-hr integrated PM2.5 concentrations varied from 15 to 157 μg·m-3, with a mean value of 53 ± 25 μg·m-3, which exceeded the 24-hr PM2.5 standard of 35μg·m-3 set by USEPA, but was below the standard of 75μg·m-3, set by China Ministry of Environmental Protection. Energy-dispersive X-ray fluorescence spectrometry (XRF) was applied to determine PM2.5 chemical element concentrations. The order of concentrations of heavy metals in PM2.5 were iron (Fe) > zinc (Zn) > manganese (Mn) > lead (Pb) > arsenic (As) > chromium (Cr). The total concentration of 18 chemical elements was 13 ± 2 μg·m-3, accounting for 25% in PM2.5, which is comparable to other major cities in China, but much higher than cities outside of China.

The Relationships between PM2.5 and Meteorological Factors in China: Seasonal and Regional Variations

The interactions between PM_2.5 and meteorological factors play a crucial role in air pollution analysis. However, previous studies that have researched the relationships between PM_2.5 concentration and meteorological conditions have been mainly confined to a certain city or district, and the correlation over the whole of China remains unclear. Whether spatial and seasonal variations exist deserves further research. In this study, the relationships between PM_2.5 concentration and meteorological factors were investigated in 68 major cities in China for a continuous period of 22 months from February 2013 to November 2014, at season, year, city, and regional scales, and the spatial and seasonal variations were analyzed. The meteorological factors were relative humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS). We found that spatial and seasonal variations of their relationships with PM_2.5 exist. Spatially, RH is positively correlated with PM_2.5 concentration in north China and Urumqi, but the relationship turns to negative in other areas of China. WS is negatively correlated with PM_2.5 everywhere except for Hainan Island. PS has a strong positive relationship with PM_2.5 concentration in northeast China and mid-south China, and in other areas the correlation is weak. Seasonally, the positive correlation between PM_2.5 concentration and RH is stronger in winter and spring. TEM has a negative relationship with PM_2.5 in autumn and the opposite in winter. PS is more positively correlated with PM_2.5 in autumn than in other seasons. Our study investigated the relationships between PM_2.5 and meteorological factors in terms of spatial and seasonal variations, and the conclusions about the relationships between PM_2.5 and meteorological factors are more comprehensive and precise than before. We suggest that the variations could be considered in PM_2.5 concentration prediction and haze control to improve the prediction accuracy and policy efficiency.

Air pollution characteristics and health risks in Henan Province, China

Events of severe air pollution occurred frequently in China recently, thus understanding of the air pollution characteristics and its health risks is very important. In this work, we analyzed a two-year dataset (March 2014 - February 2016) including daily concentrations of six criteria pollutants (PM_2.5, PM₁₀, CO, SO₂, NO₂, and O₃) from 18 cities in Henan province. Results reveal the serious air pollution status in Henan province, especially the northern part, and Zhengzhou is the city with the worst air quality. Annual average PM_2.5 concentrations exceed the second grade of Chinese Ambient Air Quality Standard (75μg/m³) at both 2014 and 2015. PM_2.5 is typically the major pollutant, but ozone pollution can be significant during summer. Furthermore, as the commonly used air quality index (AQI) neglects the mutual health effects from multiple pollutants, we introduced the aggregate air quality index (AAQI) and health-risk based air quality index (HAQI) to evaluate the health risks. Results show that based on HAQI, the current AQI system likely significantly underestimate the health risks of air pollution, highlighting that the general public may need stricter health protection measures. The population-weighted two-year average HAQI data further demonstrates that all population in the studied cities in Henan province live with polluted air - 72% of the population is exposed to air that is unhealthy for sensitive people, while 28% of people is exposed to air that can be harmful to healthy people; and the health risks are much greater during winter than during other seasons. Future works should further improve the HAQI algorithm, and validate the links between the clinical/epidemiologic data and the HAQI values.

A paradox for air pollution controlling in China revealed by "APEC Blue" and "Parade Blue"

A series of strict emission control measures were implemented in Beijing and surrounding regions to ensure good air quality during the 2014 Asia-Pacific Economic Cooperation (APEC) summit and 2015 Grand Military Parade (Parade), which led to blue sky days during these two events commonly referred to as "APEC Blue" and "Parade Blue". Here we calculated Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) and Ozone Monitoring Instrument (OMI) NO2 and HCHO results based on well known DOAS trace gas fitting algorithm and WRF-Chem model (with measured climatology parameter and newest emission inventor) simulated trace gases profiles. We found the NO2 columns abruptly decreased both Parade (43%) and APEC (21%) compared with the periods before these two events. The back-trajectory cluster analysis and the potential source contribution function (PSCF) proved regional transport from southern peripheral cities plays a key role in pollutants observed at Beijing. The diminishing transport contribution from southern air mass during Parade manifests the real effect of emission control measures on NO2 pollution. Based on the ratios of HCHO over NO2 we found there were not only limited the NO2 pollutant but also suppress the O3 contaminant during Parade, while O3 increased during the APEC.

Spatial and Temporal Variations of PM2.5 and Its Relation to Meteorological Factors in the Urban Area of Nanjing, China

The serious air pollution problem has aroused widespread public concerns in China. Nanjing city, as one of the famous cities of China, is faced with the same situation. This research aims to investigate spatial and temporal distribution characteristics of fine particulate matter (PM2.5) and the influence of weather factors on PM2.5 in Nanjing using Spearman-Rank analysis and the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method. Hourly PM2.5 observation data and daily meteorological data were collected from 1 April 2013 to 31 December 2015. The spatial distribution result shows that the Maigaoqiao site suffered the most serious pollution. Daily PM2.5 concentrations in Nanjing varied from 7.3 μg/m³ to 336.4 μg/m³. The highest concentration was found in winter and the lowest in summer. The diurnal variation of PM2.5 increased greatly from 6 to 10 a.m. and after 6 p.m., while the concentration exhibited few variations in summer. In addition, the concentration was slightly higher on weekends compared to weekdays. PM2.5 was found to exhibit a reversed relation with wind speed, relative humidity, and precipitation. Although temperature had a positive association with PM2.5 in most months, a negative correlation was observed during the whole period. Additionally, a high concentration was mainly brought with the wind with a southwest direction and several relevant factors are discussed to explain the difference of the impacts of diverse wind directions.