Characterizing multi-pollutant air pollution in China: Comparison of three air quality indices

Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism

Air pollution over China has attracted wide interest from public and academic community. PM_2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM_2.5 concentrations are essential to understand the variability of PM_2.5 and seek methods to control PM_2.5. Since 2013, the measurement of PM_2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM_2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM_2.5 concentrations_. We start with an introduction of general meteorological conditions and PM_2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM_2.5 concentrations. Next, major methods used to quantify meteorological influences on PM_2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM_2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM_2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM_2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM_2.5. The feedback effects of PM_2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM_2.5 pollution are made finally.

Personal strategies to minimise effects of air pollution on respiratory health: advice for providers, patients and the public

As global awareness of air pollution rises, so does the imperative to provide evidence-based recommendations for strategies to mitigate its impact. While public policy has a central role in reducing air pollution, exposure can also be reduced by personal choices. Qualified evidence supports limiting physical exertion outdoors on high air pollution days and near air pollution sources, reducing near-roadway exposure while commuting, utilising air quality alert systems to plan activities, and wearing facemasks in prescribed circumstances. Other strategies include avoiding cooking with solid fuels, ventilating and isolating cooking areas, and using portable air cleaners fitted with high-efficiency particulate air filters. We detail recommendations to assist providers and public health officials when advising patients and the public regarding personal-level strategies to mitigate risk imposed by air pollution, while recognising that well-designed prospective studies are urgently needed to better establish and validate interventions that benefit respiratory health in this context.

Temporal variations of six ambient criteria air pollutants from 2015 to 2018, their spatial distributions, health risks and relationships with socioeconomic factors during 2018 in China

Air pollution events occurred frequently in China, and tremendous efforts were devoted to the reduction of air pollution in recent years. Here, analysis of ambient monitoring data of six criteria air pollutants from 367 Chinese cities during 2015-2018, showed that PM_2.5, PM₁₀, SO₂ and CO were reduced significantly by 22.1%, 13.5%, 46.4% and 21.5%, respectively, NO₂ reduction was less significant (6.3%) while O₃ level instead increased over China (13.7%). Spatial distribution, seasonal, monthly and diurnal variations of the air pollutants during 2018, implicated of effective control measures, were discussed in details, especially for the five key densely populated regions of Jing-Jin-Ji (JJJ), Fen Wei Plains (FWP), Yangtze River Delta (YRD), Sichuan Basin (SCB) and Pearl River Delta (PRD). Moreover, excess health risks (ERs) of the six pollutants were estimated for 2018, and such risks was two times higher if the World Health Organization (WHO) air quality guideline rather than Chinese guideline was adopted. PM₁₀ rather than PM_2.5 was the dominant contributor to ERs, and the case with both PM_2.5 and PM₁₀ exceeding threshold values occupied ~1/3 of total days, yet contributed ~2/3 of total ERs. For 2018, the health-risk based air quality index (HAQI) was further calculated by combining health risks from multiple pollutants, and it was found that high HAQI mostly distributed in North China Plain (NCP). ~15%, ~85% and ~95% people in YRD, FWP and JJJ were exposed to polluted air (HAQI > 100), and population-normalized HAQI further added the inequality, JJJ and a small region of SCB had much higher HAQI (>280). Investigations on HAQI with socioeconomic factors show that total population, population density and built-up area presented an inverted U-shape, suggesting existence of Environmental Kuznets Curve (EKC), while a positive relationship was found between HAQI and share of secondary industry. Multiple regression analysis suggested that built-up area was the most prominent factor to HAQI, followed by the gross domestic product (GDP). The findings here demonstrate in great details the current characteristics of air pollution and its associated health risks in China, therefore providing important implications for effective air pollution control strategies in near future for different regions of China.

Impact of Open Burning Refuse on Air Quality: In the Case of "Hidar Sitaten" at Addis Ababa, Ethiopia

Open burning of refuse is one of the key sources that causes high air pollution in Metropolitan cities. This paper identifies pollutant concentration of particulate matter (PM_2.5) emission and air quality index categories with the peak hour interval on Hidar Sitaten day, and present analysis of air quality in Addis Ababa from August 2016 to November 2019. Daily records, with a 1-hour interval, of raw concentration of air pollutant and air quality index data, were obtained from the AirNow website of Addis Ababa central monitoring station. The data collected were analyzed using descriptive statistics of the mean air quality index and concentration of PM_2.5. Accordingly, the study revealed that the peak hour for high pollutant concentration emission ranges between 8 pm to 11 pm hours, and the mean air quality index was more than a moderate level. Particularly, on Hidar Sitaten in 2019 at 9 pm the maximum concentration of PM_2.5 was 8.6 times higher than WHO air quality guideline standard of daily allowance. The highest mean of air quality index and concentration of PM_2.5 recorded was 112 and 44.2 µg/m³ on 21 November 2017, respectively, and it was found to be unhealthy for sensitive groups. This implies that the concentration of PM_2.5 was harmful to people who are unusually sensitive to particulate pollution and have health problems. Therefore, public participation and strong regulations are needed on air quality management to strike a balance between a cultural practice of Hidar Sitaten and healthy air quality.

The Impacts of Different Air Pollutants on Domestic and Inbound Tourism in China

Previous studies have reported that air pollution negatively affects the tourism industry. This paper attempted to answer the following question: among different air pollutants, which one acts as the most adverse factor? The study was based on a sample of panel data covering 337 Chinese cities for the period between 2007 and 2016. Four pollutant indicators were inspected: PM 2 . 5 (particulate matter 2.5 micrometers or less in size), PM 10 (particulate matter 10 micrometers or less in size), SO 2 (sulfur dioxide), and NO 2 (nitrogen dioxide). It was found that PM 2 . 5 had a significantly negative impact on both domestic and inbound tourist arrivals. Regarding the other three pollutant indicators, except for the negative influence of NO 2 on inbound tourist arrivals, no statistically significant impact was found. This study suggests that tourism policy makers should primarily focus on PM 2 . 5 , when considering the nexus between air quality and tourism development. According to our estimates, the negative impact of PM 2 . 5 on tourism is substantial. If the PM 2 . 5 concentration in the ambient air increases by 1 μ g/m 3 (=0.001 mg/m 3 ), domestic and inbound tourist arrivals will decline by 0.482% and 1.227%, respectively. These numbers imply an average reduction of 81,855 person-times in annual domestic tourist arrivals and 12,269 in inbound tourist arrivals in each city.

Air quality and obesity at older ages in China: The role of duration, severity and pollutants

BACKGROUND

Population ageing and air pollution have emerged as two of the most pressing challenges in China. However, little evidence has explored the impact of air pollution on obesity among older adults in China.

METHODS

The China Health and Retirement Longitudinal Study-a nationally representative sample of middle-aged and older Chinese was linked to the air pollution data at the city level. Multilevel logistic models were fitted on obesity status among older people in relation to different air quality measures such as chronic exposures to severities of air pollution and pollutants.

RESULTS

Air pollution was positively associated with increased risks of general obesity and abdominal obesity among older adults (N = 4,364) especially for those with disability. The marginal effects of average air quality index (AQI) on obesity suggest that one standard deviation increase in AQI is associated with increased risks of central obesity by 2.8% (95%CI 1.7% 3.9%) and abdominal obesity by 6.2% (95%CI 4.4% 8.0%). The risk of chronic exposures to light (and moderate), heavy and severe pollution on obesity elevated in a graded fashion in line with the level of pollution. Durations of exposure to PM2.5 and PM10 were significantly associated with increased risk of obesity among older people in China.

CONCLUSIONS

Chronic exposures to severe air pollution and certain pollutants such as PM2.5 and PM10 raise the risk of obesity among older people in China and the relationships were stronger for those with disability. Future policies that target these factors might provide a promising way of enhancing the physical health of older people.

An Azure ACES Early Warning System for Air Quality Index Deteriorating

With the development of industrialization and urbanization, air pollution in many countries has become more serious and has affected people's health. The air quality has been continuously concerned by environmental managers and the public. Therefore, accurate air quality deterioration warning system can avoid health hazards. In this study, an air quality index (AQI) warning system based on Azure cloud computing platform is proposed. The prediction model is based on DFR (Decision Forest Regression), NNR (Neural Network Regression), and LR (Linear Regression) machine learning algorithms. The best algorithm was selected to calculate the 6 pollutants required for the AQI calculation of the air quality monitoring in real time. The experimental results show that the LR algorithm has the best performance, and the method of this study has a good prediction on the AQI index warning for the next one to three hours. Based on the ACES system proposed, it is hoped that it can prevent personal health hazards and help to reduce medical costs in public.

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.

Characterization and health risks of criteria air pollutants in Delhi, 2017

Severe air pollution events were observed frequently in north India in recent years especially at its capital, Delhi. Criteria air pollutants data at 10 sites for 2017 in Delhi were analyzed. The results show annual fine particulate matter (PM_2.5) concentrations exceeded the National Ambient Air Quality Standards (NAAQS) of 60 μg/m³ at all sites from 105.51 (site 10) to 143.23 μg/m³ (site 7). Sub-urban sites (site 8, 9 and 10) had lower PM_2.5 concentrations than urban sites. Coarse PM (PM₁₀) and ozone (O₃) were also important with annual averages of 399.56 μg/m³ and 75.69 ppb, respectively. Peak PM_2.5 occurred at the Diwali in early November and Christmas. Only PM₁₀ showed a significant weekly difference with a weekdays/weekends ratio of ∼1.5. PM_2.5/PM₁₀ ratio in episode days with PM_2.5 of >60 μg/m³ was higher than non-episode days. Pearson correlation coefficients show O₃ was negatively related with CO, SO₂, and NO₂, while PM_2.5 was positively related to these pollutants. Analysis of two extreme events from Nov. 6th to Nov. 14th and Dec. 18th to Dec. 26th shows that meteorological conditions with low wind speed and warm temperature kept PM_2.5 concentrations at a high level during these events. Backward trajectory and cluster analysis show the wind coming from northwest of Delhi, where massive anthropogenic emissions were generated, led to high concentrations of air pollutants to Delhi. Health risk analysis reveals that PM_2.5 and PM₁₀ were the two major pollutants threatening public health among the six criteria pollutants.

Responses of PM2.5 and O3 concentrations to changes of meteorology and emissions in China

Tremendous efforts have been made to reduce the severe air pollution in China since 2013. However, the annual and peak fine particulate matter (PM_2.5) 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 PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 control measures in China, and highlights the importance of appropriate joint reduction of NOx and VOCs to simultaneously decrease O₃ and PM_2.5 for higher air quality.

Investigating the PM2.5 mass concentration growth processes during 2013-2016 in Beijing and Shanghai

The North China Plain and the Yangtze River Delta are the two of the most heavily polluted regions in China. Observational studies revealed that 'explosive' PM_2.5 mass concentration growths frequently occurred in the two regions. This study analyzed all the PM_2.5 mass concentration growth processes from clean condition (i.e., <35 μg m^-3) to heavy pollution condition (i.e., >150 μg m^-3) in Beijing (BJ) and Shanghai (SH), two representative cities of the two regions, using hourly monitored PM_2.5 concentrations during 2013-2016. 173 and 76 growth processes were identified in BJ and SH, respectively. PM_2.5 rising rates (PMRR) and dynamic growth durations were calculated to illustrate the characteristics of the growth processes. Hourly particulate chemical composition data and meteorological data in BJ and SH were further analyzed. The 4-year averaged PMRR of PM_2.5 total mass were similarly of 7.11 ± 9.82 μg m^-3 h^-1 in BJ and 6.71 ± 6.89 μg m^-3 h^-1 in SH. A decreasing trend was found for the PM_2.5 growth processes in two cities from 2013 to 2016, reflecting the effectiveness of emission controls implemented in the past years. The contributions of particulate components to the PM_2.5 total mass growth were different in BJ and SH. Average PMRR value of PM₁ organic aerosols (OA), SO₄^2-, NO₃^-, and NH₄⁺ in BJ was 1.90, 0.95, 0.82, and 0.53 μg m^-3 h^-1, respectively. Average PMRR of PM_2.5 OA, SO₄^2-, NO₃^-, and NH₄⁺ in SH was 1.70, 1.18, 1.99 and 1.14 μg m^-3 h^-1, respectively. Based on the contributions of different components, the PM_2.5 mass concentration growth processes in BJ and SH were proposed to be classified into 'other components-dominant growth processes', 'all components-contributing growth processes', 'one or more explosive secondary components-dominant growth processes', and 'mixed-factor growth processes'. Potential source contribution function analysis and the meteorological condition analysis showed that source origins and prevailing wind for the two cities during different categories of growth processes had substantial difference. The important source areas included Hebei and Shandong for BJ, and Jiangsu and Anhui for SH. The dominant wind directions during growth processes were northeast, south and southwest in BJ, and were west to north in SH. The results suggested the contributing components, the prevailing wind conditions, and the formation processes were substantially different in the two cities, despite the similar PMRR of PM_2.5 total mass during the growth processes between BJ and SH. Future research is needed to study the detailed formation mechanisms of the different PM_2.5 mass concentration growth processes in the two cities.

Directional dependence between major cities in China based on copula regression on air pollution measurements

Air pollution is well-known as a major risk to public health, causing various diseases including pulmonary and cardiovascular diseases. As social concern increases, the amount of air pollution data is increasing rapidly. The purpose of this study is to statistically characterize dependence between major cities in China based on a measure of directional dependence estimated from PM2.5 measurements. As a measure of the directional dependence, we propose the so-called copula directional dependence (CDD) using beta regression models. An advantage of the CDD is that it does not rely on strict assumptions of specific probability distributions or linearity. We used hourly PM2.5 measurement data collected at four major cities in China: Beijing, Chengdu, Guangzhou, and Shanghai, from 2013 to 2017. After accounting for autocorrelation in the PM2.5 time series via nonlinear autoregressive models, CDDs between the four cities were estimated to produce directed network structures of statistical dependence. In addition, a statistical method was proposed to test the directionality of dependence between each pair of cities. From the PM2.5 data, we could discover that Chengdu and Guangzhou are the most closely related cities and that the directionality between them has changed once during 2013 to 2017, which implies a major economic or environmental change in these Chinese regions.

Ambient air pollution and the risk of stillbirth: a population-based prospective birth cohort study in the coastal area of China

Accumulating evidence has shown that prenatal ambient air pollution exposure is associated with elevated stillbirth risk; however, the results are inconsistent. This population-based prospective cohort study aimed to explore the association between prenatal air pollution exposure and stillbirth rate in the coastal area in China. Data of air pollution and birth outcomes between January 1, 2015, and December 31, 2017, were collected. Among the 59,868 eligible births, there were 587 stillbirths and 59,281 live births. Although the air quality in this study was relatively better than most of the major cities in China, a positive association was still found between prenatal air pollution exposure and stillbirth rate. Every 10 μg/m³ increase of fine particulate matters (PM_2.5) in each trimester, as well as in the entire pregnancy, was associated with increased stillbirth rate (RR = 1.14, 1.11, 1.15, and 1.14 for the first, second, third trimester, and entire pregnancy, respectively). In addition, every 10 μg/m³ increase of PM₁₀ in the first trimester (RR = 1.09, 95% CI: 1.04-1.14), and 10 μg/m³ increase of O₃ in the first (RR = 1.05, 95% CI: 1.01-1.09) and third (RR = 1.04, 95% CI: 1.00-1.08) trimesters was also associated with increased stillbirth rate. The effects of PM_2.5 on stillbirth rate were found to be robust in the two-pollutant models. The findings of this study especially underscored the adverse effects of prenatal exposure of high levels of PM_2.5 on stillbirth. More studies are needed to verify our findings and further investigate the underlying mechanisms.

Spatiotemporal Features and Socioeconomic Drivers of PM2.5 Concentrations in China

Fine particulate matter (PM2.5) has been an important environmental issue because it can seriously harm human health and can adversely affect the economy. It poses a problem worldwide and especially in China. Based on data of PM2.5 concentration and night light data, both collected from satellite remote sensing during 1998–2013 in China, we identify the socio-economic determinants of PM2.5 pollution by taking into account the spatial flow and diffusion of regional pollutants. Our results show PM2.5 pollution displays the remarkable feature of spatial agglomeration. High concentrations of PM2.5 are mainly found in Eastern China (including Shandong, Jiangsu, and Anhui provinces) and the Jing-Jin-Ji Area region in the north of China (including Beijing, Tianjin, and Hebei provinces) as well as in the Henan provinces in central China. There is a significant positive spatial spillover effect of PM2.5 pollution, so that an increase in PM2.5 concentration in one region contributes to an increase in neighboring regions. Whether using per capita GDP or nighttime lighting indicators, there is a significant N-shaped curve that relates PM2.5 concentration and economic growth. Population density, industrial structure, and energy consumption have distinct impacts on PM2.5 pollution, while urbanization is negative correlated with PM2.5 emissions. As a result, policies to strengthen regional joint prevention and control, implement cleaner manufacturing techniques, and reduce dependence on fossil fuels should be considered by policy makers for mitigating PM2.5 pollution.

Pollution characteristics in a dusty season based on highly time-resolved online measurements in northwest China

To investigate the pollution characteristics and potential sources in a dusty season, an online analyzer was used to measure trace gases and major water-soluble ions in PM₁₀ from April 1st to May 29th, 2011 in Lanzhou. The average concentrations of HONO, HNO₃, HCl, SO₂ and NH₃ were 0.93, 1.16, 0.48, 9.29 and 5.54 μg/m³, respectively, and 2.8, 2.76, 8.28 and 2.48 μg/m³ for Cl^-, NO₃^-, SO₄^2- and NH₄⁺. In the non-dust period, diurnal variations of SO₄^2-, NO₃^- and their gaseous precursors showed similar change trend. NH₄⁺ showed unimodal pattern whereas NH₃ illustrated a bimodal pattern. HCl and Cl^- showed an opposite diurnal pattern. In the dust event, temporal profiles of HCl and Cl^-, SO₂ and SO₄^2- all presented similar change trend, and SO₄^2- and Cl^- preceded dust ions (Ca²⁺ and Mg²⁺) 13 h. The ratios of NO₃^- to SO₄^2- were 0.65 in the non-dust period and 0.31 in the dust event. In the dust event, the sulfur oxidation ratio (SOR) was a factor of 1.33 greater than that in the non-dust period, and [SO₄^2-]/[SO₂] was 2.31 times of that in the non-dust period. The source apportionment using Probabilistic Matrix Factorization (PMF) suggested that fugitive dust (58.09%), secondary aerosols (33.98%), and biomass burning (7.93%) were the major sources in the non-dust period whereas dust (67.01%), salt lake (29.68%), biomass burning (0.8%), and motor vehicle (2.51%) were the primary sources in the dust event. Concentration weighted trajectory (CWT) model indicated that NO₃^-, Cl^- and K⁺ could be regarded as local source species, the potential sources of Na⁺, Mg²⁺ and Ca²⁺ concentrated in the two large areas with the one covered in the junction areas of Xinjiang, Qinghai and Gansu and another one covered the places around in Lanzhou, the potential sources of SO₄^2- were mainly localized in the areas adjacent to Lanzhou.

Associations between daily outpatient visits for respiratory diseases and ambient fine particulate matter and ozone levels in Shanghai, China

Air pollution in China has been very serious during the recent decades. However, few studies have investigated the effects of short-term exposure to PM_2.5 and O₃ on daily outpatient visits for respiratory diseases. We examined the effects of PM_2.5 and O₃ on the daily outpatient visits for respiratory diseases, explored the sensitivities of different population subgroups and analyzed the relative risk (RR) of PM_2.5 and O₃ in different seasons in Shanghai during 2013-2016. The generalized linear model (GLM) was applied to analyze the exposure-response relationship between air pollutants (daily average PM_2.5 and daily maximum 8-h average O₃), and daily outpatient visits due to respiratory diseases. The sensitivities of males and females at the ages of 15-60 yr-old and 60+ yr-old to the pollutants were also studied for the whole year and for the cold and warm months, respectively. Finally, the results of the single-day lagged model were compared with that of the moving average lag model. At lag 0 day, the RR of respiratory outpatients increased by 0.37% with a 10 μg/m³ increase in PM_2.5. Exposure to PM_2.5 (RR, 1.0047, 95% CI, 1.0032-1.0062) was more sensitive for females than for males (RR, 1.0025, 95% CI, 1.0008-1.0041), and was more sensitive for the 15-60 yr-old (RR, 1.0041, 95% CI, 1.0027-1.0055) than the 60+ yr-old age group (RR, 1.0031, 95% CI, 1.0014-1.0049). O₃ was not significantly associated with respiratory outpatient visits during the warm periods, but was negatively associated during the cold periods. PM_2.5 was more significantly in the cold periods than that in the warm periods. The results indicated that control of PM_2.5, compared to O₃, in the cold periods would be more beneficial to the respiratory health in Shanghai. In addition, the single-day lagged model underestimated the relationship between PM_2.5 and O₃ and outpatient visits for respiratory diseases compared to the moving average lag model.

Low-temperature co-purification of NOx and Hg0 from simulated flue gas by CexZryMnzO2/r-Al2O3: the performance and its mechanism

In this study, series of Ce_xZr_yMn_zO₂/r-Al₂O₃ catalysts were prepared by impregnation method and explored to co-purification of NO_x and Hg⁰ at low temperature. The physical and chemical properties of the catalysts were investigated by XRD, BET, FTIR, NH₃-TPD, H₂-TPR, and XPS. The experimental results showed that 10% Ce_0.2Zr_0.3Mn_0.5O₂/r-Al₂O₃ yielded higher conversion on co-purification of NO_x and Hg⁰ than the other prepared catalysts at low temperature, especially at 200-300 °C. 91% and 97% convert rate of NO_x and Hg⁰ were obtained, respectively, when 10% Ce_0.2Zr_0.3Mn_0.5O₂/r-Al₂O₃ catalyst was used at 250 °C. Moreover, the presence of H₂O slightly decreased the removal of NO_x and Hg⁰ owing to the competitive adsorption of H₂O and Hg⁰. When SO₂ was added, the removal of Hg⁰ first increased slightly and then presented a decrease due to the generation of SO₃ and (NH₄)₂SO₄. The results of NH₃-TPD indicated that the strong acid of 10% Ce_0.2Zr_0.3Mn_0.5O₂/r-Al₂O₃ improved its high-temperature activity. XPS and H₂-TPR results showed there were high-valence Mn and Ce species in 10% Ce_0.2Zr_0.3Mn_0.5O₂/r-Al₂O₃, which could effectively promote the removal of NO_x and Hg⁰. Therefore, the mechanisms of Hg⁰ and NO_x removal were proposed as Hg (ad) + [O] → HgO (ad), and 2NH₃/NH₄⁺ (ad) + NO₂ (ad) + NO (g) → 2 N₂ + 3H₂O/2H⁺, respectively. Graphical abstract ᅟ.

Characterization and source identification of fine particulate matter in urban Beijing during the 2015 Spring Festival

The Spring Festival (SF) is the most important holiday in China for family reunion and tourism. During the 2015 SF an intensive observation campaign of air quality was conducted to study the impact of the anthropogenic activities and the dynamic characteristics of the sources. During the study period, pollution episodes frequently occurred with 12days exceeding the Chinese Ambient Air Quality Standards for 24-h average PM_2.5 (75μg/m³), even 8days with exceeding 150μg/m³. The daily maximum PM_2.5 concentration reached 350μg/m³ while the hourly minimum visibility was <0.8km. Three pollution episodes were selected for detailed analysis including chemical characterization and diurnal variation of the PM_2.5 and its chemical composition, and sources were identified using the Positive Matrix Factorization model. The first episode occurring before the SF was characterized by more formation of SO₄^2- and NO₃^- and high crustal enrichment factors for Ag, As, Cd, Cu, Hg, Pb, Se and Zn and seven categories of pollution sources were identified, whereby vehicle emission contributed 38% to the PM_2.5. The second episode occurring during the SF was affected heavily by large-scale firework emissions, which led to a significant increase in SO₄^2-, Cl^-, OC, K and Ba; these emissions were the largest contributor to the PM_2.5 accounting for 36%. During the third episode occurring after the SF, SO₄^2-, NO₃^-, NH₄⁺ and OC were the major constituents of the PM_2.5 and the secondary source was the dominant source with a contribution of 46%. The results provide a detailed understanding on the variation in occurrence, chemical composition and sources of the PM_2.5 as well as of the gaseous pollutants affected by the change in anthropogenic activities in Beijing throughout the SF. They highlight the need for limiting the firework emissions during China's most important traditional festival.

Mortality effects of heat waves vary by age and area: a multi-area study in China

BACKGROUND

Many studies have reported an increased mortality risk from heat waves comparing with non-heat wave days. However, how much the mortality rate change with the heat intensity-vulnerability curve-is still unknown. Such unknown information makes the related managers impossible to assess scientifically life losses from heat waves, consequently fail in conducting suitable integrated risk management measures.

METHODS

We used the heat wave intensity index (HWII) to characterize quantitatively the heat waves, then applied a distributed lag non-linear model to explore the area-specific definition of heat wave, and developed the vulnerability models on the relationships between HWII and mortality by age and by area. Finally, Monte Carlo method was run to assess and compare the event-based probabilistic heat wave risk during the periods of 1971-2015 and 2051-2095.

RESULTS

We found a localized definition of heat wave for each corresponding area based on the minimum AIC (Akaike information criterion). Under the local heat wave events, the expected life loss during 1971-2015 does distinguish across areas, and decreases consistently in the order of WZ Chongqing, PK Nanjing and YX Guangzhou for each age group. More specifically, for the elders (≥65), the average annual loss (AAL) (and 95% confidence interval) would be 61.3 (30.6-91.9), 38 (3.8-72.2) and 18.7 (7.3-30) deaths per million people. With two stresses from warming and aging in future China, the predicted average AAL of the elders under four Representative Carbon Pathways (2.6, 4.5, 6.0, and 8.5) during 2051-2095 would be 2460, 1675, 465 deaths per million for PK Nanjing, YX Guangzhou and WZ Chongqing, respectively, approximately becoming 8~ 90 times of the AAL during 1971-2015.

CONCLUSION

This study found that the non-linear HWII-mortality relationships vary by age and area. The heat wave mortality losses are closely associated with the social-economic level. With the increasing extreme climatic events and a rapid aging trend in China, our findings can provide guidance for policy-makers to take appropriate regional adaptive measures to reduce health risks in China.

Sources of particulate matter in China: Insights from source apportionment studies published in 1987-2017

Particulate matter (PM) in the atmosphere has adverse effects on human health, ecosystems, and visibility. It also plays an important role in meteorology and climate change. A good understanding of its sources is essential for effective emission controls to reduce PM and to protect public health. In this study, a total of 239 PM source apportionment studies in China published during 1987-2017 were reviewed. The documents studied include peer-reviewed papers in international and Chinese journals, as well as degree dissertations. The methods applied in these studies were summarized and the main sources in various regions of China were identified. The trends of source contributions at two major cities with abundant studies over long-time periods were analyzed. The most frequently used methods for PM source apportionment in China are receptor models, including chemical mass balance (CMB), positive matrix factorization (PMF), and principle component analysis (PCA). Dust, fossil fuel combustion, transportation, biomass burning, industrial emission, secondary inorganic aerosol (SIA) and secondary organic aerosol (SOA) are the main source categories of fine PM identified in China. Even though the sources of PM vary among seven different geographical areas of China, SIA, industrial, and dust emissions are generally found to be the top three source categories in 2007-2016. A number of studies investigated the sources of SIA and SOA in China using air quality models and indicated that fossil fuel combustion and industrial emissions were the most important sources of SIA (total contributing 63.5%-88.1% of SO₄^2-, and 47.3%-70% NO₃^-), and agriculture emissions were the dominant source of NH₄⁺ (contributing 53.9%-90%). Biogenic emissions were the most important source of SOA in China in summer, while residential and industrial emissions were important in winter. Long-term changes of PM sources at two megacities of Beijing and Nanjing indicated that the contributions of fossil fuel and industrial sources have been declining after stricter emission controls in recent years. In general, dust and industrial contributions decreased and transportation contributions increased after 2000. PM_2.5 emissions are predicted to decline in most regions during 2005-2030, even though the energy consumptions except biomass burning are predicted to continue to increase. Industrial, residential, and biomass burning sources will become more important in the future in the businuess-as-usual senarios. This review provides valuable information about main sources of PM and their trends in China. A few recommendations are suggested to further improve our understanding the sources and to develop effective PM control strategies in various regions of China.

Photochemical Conversion of Surrogate Emissions for Use in Toxicological Studies: Role of Particulate- and Gas-Phase Products

The production of photochemical atmospheres under controlled conditions in an irradiation chamber permits the manipulation of parameters that influence the resulting air-pollutant chemistry and potential biological effects. To date, no studies have examined how contrasting atmospheres with a similar Air Quality Health Index (AQHI), but with differing ratios of criteria air pollutants, might differentially affect health end points. Here, we produced two atmospheres with similar AQHIs based on the final concentrations of ozone, nitrogen dioxide, and particulate matter (PM2.5). One simulated atmosphere (SA-PM) generated from irradiation of ∼23 ppmC gasoline, 5 ppmC α-pinene, 529 ppb NO, and 3 μg m-3 (NH4)2SO4 as a seed resulted in an average of 976 μg m-3 PM2.5, 326 ppb NO2, and 141 ppb O3 (AQHI 97.7). The other atmosphere (SA-O3) generated from 8 ppmC gasoline, 5 ppmC isoprene, 874 ppb NO, and 2 μg m-3 (NH4)2SO4 resulted in an average of 55 μg m-3 PM2.5, 643 ppb NO2, and 430 ppb O3 (AQHI of 99.8). Chemical speciation by gas chromatography showed that photo-oxidation degraded the organic precursors and promoted the de novo formation of secondary reaction products such as formaldehyde and acrolein. Further work in accompanying papers describe toxicological outcomes from the two distinct photochemical atmospheres.

Different response of human mortality to extreme temperatures (MoET) between rural and urban areas: A multi-scale study across China

BACKGROUND

The environmental variation in mortality due to extreme temperatures has been well-documented by many studies. Mortality to extreme temperatures (MoET) was recognized to vary geographically, either by countries within a region or by areas within a country. However, so far, little attention has been paid to rural residents, with even lesser attention on the potential rural-urban differences. The aim of our study was to offer a quite comprehensive analysis on the differences in temperature-mortality relationship between rural and urban areas across China.

METHOD

A distributed lag nonlinear model was built to describe the temperature-mortality relationship, based on the mortality data and meteorological variable of 75 communities in China from 2007 to 2012. Subsequently, a meta-analysis was applied to compare the differences in the temperature-mortality relationship between rural and urban areas at various levels.

RESULTS

Distinct responses regarding MoET between rural and urban areas were observed at different spatial scales. At regional level, more U-shaped curves were observed for temperature-mortality relationships in urban areas, while more J-shaped curves were observed in rural areas. At national scale, we found that the cold effect was stronger in rural areas (RR: rural 1.69 vs. urban 1.51), while heat effect was stronger in urban areas (RR: rural 1.01 vs. urban 1.12). Moreover, the modifying influence of air pollution on temperature-mortality relationship was found to be very limited.

CONCLUSION

The difference in response of MoET between rural and urban areas was noticeable, cold effect is more significant in China both in rural and urban areas. Additionally, urban areas in southern China and rural areas in northern China suffered more from extreme temperature events. Our findings suggest that differences in rural-urban responses to MoET should be taken seriously when intervention measures for reducing the risks to residents' health were adopted.

Estimating the Excess Mortality Risk during Two Red Alert Periods in Beijing, China

The magnitude of excess mortality risk due to exposures to heavy air pollution during the red alert periods in Beijing remains unknown. A health impact assessment tool combined with the PM2.5-mortality relationship was applied to estimate the number of excess deaths due to high air pollution exposure during two red alert periods in Beijing, China in December 2015. Daily PM2.5 concentration increased from 80.2 µg/m³ to 159.8 µg/m³ during the first red alert period and from 61.9 µg/m³ to 226 µg/m³ during the second period in 2015 when compared to daily PM2.5 concentrations during the same calendar date of 2013 and 2014. It was estimated that 26 to 42 excessive deaths (including 14 to 34 cardiovascular deaths, and four to 16 respiratory deaths) occurred during the first period, and 40 to 65 excessive deaths (22 to 53 cardiovascular deaths, and six to 13 respiratory deaths) occurred during the second period. The results show that heavy smog may have substantially increased the mortality risk in Beijing, suggesting more stringent air pollution controlling measures should be implemented to protect the public health.

Source apportionment of fine particulate matter in China in 2013 using a source-oriented chemical transport model

China has been suffering high levels of fine particulate matter (PM_2.5). Designing effective PM_2.5 control strategies requires information about the contributions of different sources. In this study, a source-oriented Community Multiscale Air Quality (CMAQ) model was applied to quantitatively estimate the contributions of different source sectors to PM_2.5 in China. Emissions of primary PM_2.5 and gas pollutants of SO₂, NO_x, and NH₃, which are precursors of particulate sulfate, nitrate, and ammonium (SNA, major PM_2.5 components in China), from eight source categories (power plants, residential sources, industries, transportation, open burning, sea salt, windblown dust and agriculture) were separately tracked to determine their contributions to PM_2.5 in 2013. Industrial sector is the largest source of SNA in Beijing, Xi'an and Chongqing, followed by agriculture and power plants. Residential emissions are also important sources of SNA, especially in winter when severe pollution events often occur. Nationally, the contributions of different source sectors to annual total PM_2.5 from high to low are industries, residential sources, agriculture, power plants, transportation, windblown dust, open burning and sea salt. Provincially, residential sources and industries are the major anthropogenic sources of primary PM_2.5, while industries, agriculture, power plants and transportation are important for SNA in most provinces. For total PM_2.5, residential and industrial emissions are the top two sources, with a combined contribution of 40-50% in most provinces. The contributions of power plants and agriculture to total PM_2.5 are about 10%, respectively. Secondary organic aerosol accounts for about 10% of annual PM_2.5 in most provinces, with higher contributions in southern provinces such as Yunnan (26%), Hainan (25%) and Taiwan (21%). Windblown dust is an important source in western provinces such as Xizang (55% of total PM_2.5), Qinghai (74%), Xinjiang (59%). The large variation in sources of PM_2.5 across China suggests that PM_2.5 mitigation programs should be designed separately for different regions/provinces.

Causative impact of air pollution on evapotranspiration in the North China Plain

Atmospheric dispersion conditions strongly impact air pollution under identical surface emissions. The degree of air pollution in the Jing-Jin-Ji region is so severe that it may impose feedback on local climate. Reference evapotranspiration (ET₀) plays a significant role in the estimation of crop water requirements, as well as in studies on climate variation and change. Since the traditional correlation analysis cannot capture the causality, we apply the convergent cross mapping method (CCM) in this study to observationally investigate whether the air pollution impacts ET₀. The results indicate that southwest regions of Jing-Jin-Ji always suffer higher PM_2.5 concentration than north regions through the whole year, and correlation analysis suggests that PM_2.5 concentration has a significant negative effect on ET₀ in most cities. The causality detection with CCM quantitatively demonstrates the significantly causative influence of PM_2.5 concentration on ET₀, higher PM_2.5 concentration decreasing ET₀. However, CCM analysis suggests that PM_2.5 concentration has a relatively weak causal influence on ET₀ while the correlation analysis gives the near zero correlation coefficient in Zhangjiakou city, indicating that the causative influence of PM_2.5 concentration on ET₀ is better revealed with CCM method than the correlation analysis. Considering that ET₀ is strongly associated with crop water requirement, the amount of water for agricultural irrigation could be reduced at high PM_2.5 concentrations. These findings can be utilized to improve the efficiency of water resources utilization, and reduce the exploiting amount of groundwater in the Jing-Jin-Ji region, although PM_2.5 is detrimental to human health.

Investigation of submicron aerosol characteristics in Changzhou, China: Composition, source, and comparison with co-collected PM2.5

Mass concentrations and chemical compositions of submicron particles (PM₁) collected during July 2015 to April 2016 in Changzhou, a city in the Yangtze River Delta region, were systematically investigated for the first time. Specifically, an Aerodyne soot particle aerosol mass spectrometer (SP-AMS) was employed to characterize the water-soluble organic matter (WSOM). The average concentration of PM₁ was 63.6 μg m^-3, occupying ∼60% of co-collected PM_2.5 mass. Water soluble inorganic ions (WSIIs) was the most abundant component with secondary ions (SO₄^2-, NO₃^- and NH₄⁺) as the dominant species. Organic matter (OM) accounted for 21.6% of PM₁, with approximately 80% was water-soluble. Trace metals could constitute up to 3.0% of PM₁ mass, and Fe, Al and Zn were the three most abundant ones. PAHs were predominated by ones with 5-6 rings, occupying over half of the PAHs mass; further analyses showed that fuel and coal combustion had significant contributions to PAHs. Positive matrix factorization of the WSOM data separated four factors: a traffic-related hydrocarbon-like OA (HOA), a local OA (LOA) likely associated with cooking and coal combustion emissions, etc., a secondary nitrogen-enriched OA (NOA) and an oxygenated OA (OOA). PCA analyses showed that crustal source was likely important for PM₁ too. Back trajectory results implied that both PM₁ and PM_2.5 were mainly derived from local/regional emissions. Our findings present results regarding the PM₁ chemistry and its relationship with the PM_2.5 in Changzhou, which are valuable for the government to make effective policies to reduce the aerosol pollution in and near the city.

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.

Difference of performance in response to disease admissions between daily time air quality indices and those derived from average and entropy functions

Daily time air quality indices, which can reflect air quality in 1 day, are suitable for identifying daily exposure during conditions of poor air quality. The aim of this study is to compare the main effectiveness of four daily time indices in representing variation in the number of disease admissions. These indices include pollution standard index (PSI), air quality index (AQI) and their respective indices derived from mean and entropy functions: MEPSI and MEAQI. The hourly concentrations of fine particulate matter less than 10 μm in diameter (PM₁₀), PM_2.5, O₃, CO, NO₂ and SO₂ from 1 January 2006 to 31 December 2010 were obtained from 14 air quality monitoring stations owned by the Environmental Protection Administration (EPA) in the Kaoping region, Taiwan.Instead of circulatory system disease admissions, the indices were correlative with the number of respiratory disease admissions with correlative coefficients of 0.49 to 0.56 (P < 0.05). The daily time air quality indices derived from mean and entropy functions improved their performance of reactive range and air pollution identification. The reactive range of MEPSI and MEAQI was 1.4-3 times that of the original indices. The MEPSI and MEAQI increased identification from 40 to 180 in index scale and revealed one to two additional categories of public health effect information. In comparison with other indices, MEAQI is more effective for application to pollution events with multiple air pollutants.

Characterization of criteria air pollutants in Beijing during 2014-2015

One year-long criteria air pollutants data collected in Beijing were analyzed in this paper, which can support the research on formation, transport and human health effects of air pollutants. This is the first time to study the spatial and temporal variations of criteria pollutants in Beijing using hourly observational data from 12 sites between June 2014 and May 2015 released by the Ministry of Environmental Protection (MEP) of China. Beijing is facing tremendous air pollution as the daily averaged PM_2.5 (particulate matter with aerodynamic diameter less than 2.5µm) concentrations in all sites exceeding the Chinese Ambient Air Quality Standards (CAAQS) Grade I & II standards (15 and 35µg/m³). Slightly differences in PM_2.5 and ozone (O₃) were observed between sites at the urban and rural areas. Pearson correlation coefficients show that most pollutants are temporally correlated in Beijing except for O₃. The coefficients of divergence (COD) indicate that PM_2.5 is associated at most sites with only one rural site (Dingling) having observable difference and one site may be insufficient for monitoring surrounding area. The 8h peak O₃ (O₃-8h) also correlates at different sites but with one urban site (Haidianquwanliu) different from others. In addition, an extreme PM_2.5 event (hourly average concentration exceeding 300μg/m³ for ~40h) was examined with the consideration of meteorological conditions. Southerly wind with low speed and high relative humidity allow the accumulation of pollutants while northerly wind with high speed and low relative humidity result in good air quality.

Detecting the causality influence of individual meteorological factors on local PM2.5 concentration in the Jing-Jin-Ji region

Due to complicated interactions in the atmospheric environment, quantifying the influence of individual meteorological factors on local PM_2.5 concentration remains challenging. The Beijing-Tianjin-Hebei (short for Jing-Jin-Ji) region is infamous for its serious air pollution. To improve regional air quality, characteristics and meteorological driving forces for PM_2.5 concentration should be better understood. This research examined seasonal variations of PM_2.5 concentration within the Jing-Jin-Ji region and extracted meteorological factors strongly correlated with local PM_2.5 concentration. Following this, a convergent cross mapping (CCM) method was employed to quantify the causality influence of individual meteorological factors on PM_2.5 concentration. The results proved that the CCM method was more likely to detect mirage correlations and reveal quantitative influences of individual meteorological factors on PM_2.5 concentration. For the Jing-Jin-Ji region, the higher PM_2.5 concentration, the stronger influences meteorological factors exert on PM_2.5 concentration. Furthermore, this research suggests that individual meteorological factors can influence local PM_2.5 concentration indirectly by interacting with other meteorological factors. Due to the significant influence of local meteorology on PM_2.5 concentration, more emphasis should be given on employing meteorological means for improving local air quality.

Influence of air pollution on exhaled carbon monoxide levels in smokers and non-smokers. A prospective cross-sectional study

BACKGROUND

The poor air quality and cigarette smoking are the most important reasons for increased carbon monoxide (CO) level in exhaled air. However, the influence of high air pollution concentration in big cities on the exhaled CO level has not been well studied yet.

OBJECTIVES

To evaluate the impact of smoking habit and air pollution in the place of living on the level of CO in exhaled air.

METHODS

Citizens from two large cities and one small town in Poland were asked to complete a survey disclosing their place of residence, education level, work status and smoking habits. Subsequently, the CO level in their exhaled air was measured. Air quality data, obtained from the Regional Inspectorates of Environmental Protection, revealed the differences in atmospheric CO concentration between locations.

RESULTS

1226 subjects were divided into 4 groups based on their declared smoking status and place of living. The average CO level in exhaled air was significantly higher in smokers than in non-smokers (p<0.0001) as well as in non-smokers from big cities than non-smokers from small ones (p<0.0001). Created model showed that non-smokers from big cities have odds ratio of 125.3 for exceeding CO cutoff level of 4ppm compared to non-smokers from small towns.

CONCLUSIONS

The average CO level in exhaled air is significantly higher in smokers than non-smokers. Among non-smokers, the average exhaled CO level is significantly higher in big city than small town citizens. These results suggest that permanent exposure to an increased concentration of air pollution and cigarette smoking affect the level of exhaled CO.

The covariance of air quality conditions in six cities in Southern Germany - The role of meteorology

This paper analyzed air quality in six cities in Southern Germany (Ulm, Augsburg, Konstanz, Freiburg, Stuttgart and Munich), in conjunction with the prevailing synoptic conditions. Air quality was estimated through the calculation of a daily Air Stress Index (ASI) constituted by five independent components, each one expressing the contribution of one of the five main pollutants (PM₁₀, O₃, SO₂, NO₂ and CO) to the total air stress. As it was deduced from ASI components, PM₁₀ from combustion sources and photochemically produced tropospheric O₃ are the most hazardous pollutants at the studied sites, throughout cold and warm periods respectively, yet PM₁₀ contribute substantially to the overall air stress during both seasons. The influence of anticyclonic high pressure systems, leading to atmospheric stagnation, was associated with increased ASI values, mainly due to the entrapment of PM₁₀. Moderate air stress was generally estimated in all cities however a cleaner atmosphere was detected principally in Freiburg when North Europe was dominated by low pressure systems. Daily events of notably escalated ASI values were further analyzed with backward air mass trajectories. Throughout cold period, ASI episodes were commonly related to eastern airflows carrying exogenous PM₁₀ originated from eastern continental Europe. During warm period, ASI episodes were connected to the arrival of regionally circulated air parcels reflecting lack of dispersion and accumulation of pollutants in accordance with the synoptic analysis.

Tackling air pollution and extreme climate changes in China: Implementing the Paris climate change agreement

China still depends on coal for more than 60% of its power despite big investments in the process of shifting to nuclear, solar and wind power renewable energy resources alignment with Paris climate change agreement (Paris CCA). Chinese government through the Communist Party Central Committee (CPCC) ascribes great importance and commitment to Paris CCA legacy and history landmark implementation at all levels. As the world's biggest carbon dioxide emitter, China has embarked on "SMART" pollution and climate changes programs and measures to reduce coal-fired power plants to less than 50% in the next five years include: new China model of energy policies commitment on CO2 and greenhouse gas emissions reductions to less than 20% non-fossil energy use by 2030 without undermining their economic growth, newly introduced electric vehicles transportation benefits, interactive and sustained air quality index (AQI) monitoring systems, decreasing reliance on fossil fuel economic activities, revision of energy price reforms and renewable energy to less energy efficient technologies development. Furthermore, ongoing CPCC improved environmental initiatives, implemented strict regulations and penalties on local companies and firms' pollution production management, massive infrastructures such as highways to reduce CO2 expansion of seven regional emissions trading markets and programs for CO2 emissions and other pollutants are being documented. Maximizing on the centralized nature of the China's government, implemented Chinese pollution, climate changes mitigation and adaptation initiatives, "SMART" strategies and credible measures are promising. A good and practical example is the interactive and dynamic website and database covering 367 Chinese cities and providing real time information on environmental and pollution emissions AQI. Also, water quality index (WQI), radiation and nuclear safety monitoring and management systems over time and space. These are ongoing Chinese valuable and exemplary leadership in Paris CCA implementation to the global community. Especially to pragmatic and responsible efforts to support pollution and climate changes capacity development, technology transfer and empowerment in emissions surveillance and monitoring systems and "SMART" integrated climate changes mitigation packages in global Sustainable Development Goals (SDGs) context, citizenry health and wellbeing.

Source contributions and regional transport of primary particulate matter in China

A source-oriented CMAQ was applied to determine source sector/region contributions to primary particulate matter (PPM) in China. Four months were simulated with emissions grouped to eight regions and six sectors. Predicted elemental carbon (EC), primary organic carbon (POC), and PPM concentrations and source contributions agree with measurements and have significant spatiotemporal variations. Residential is a major contributor to spring/winter EC (50-80%), POC (60%-90%), and PPM (30-70%). For summer/fall, industrial contributes 30-50% for EC/POC and 40-60% for PPM. Transportation is more important for EC (20-30%) than POC/PPM (<5%). Open burning is important in summer/fall of Guangzhou and Chongqing. Dust contributes to 1/3-1/2 in spring/fall of Beijing, Xi'an and Chongqing. Based on sector-region combination, local residential/transportation and residential/industrial from Heibei are major contributors to spring PPM in Beijing. In summer/fall, local industrial is the largest. In winter, residential/industrial from local and Hebei account for >90% in Beijing.