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

Characterization of air pollution and associated health risks in Gansu Province, China from 2015 to 2022

Air pollution poses a major threat to both the environment and public health. The air quality index (AQI), aggregate AQI, new health risk-based air quality index (NHAQI), and NHAQI-WHO were employed to quantitatively evaluate the characterization of air pollution and the associated health risk in Gansu Province before (P-I) and after (P-II) COVID-19 pandemic. The results indicated that AQI system undervalued the comprehensive health risk impact of the six criteria pollutants compared with the other three indices. The stringent lockdown measures contributed to a considerable reduction in SO2, CO, PM2.5, NO2 and PM10; these concentrations were 43.4%, 34.6%, 21.4%, 17.4%, and 14.2% lower in P-II than P-I, respectively. But the concentration of O3 had no obvious improvement. The higher sandstorm frequency in P-II led to no significant decrease in the ERtotal and even resulted in an increase in the average ERtotal in cities located in northwestern Gansu from 0.78% in P-I to 1.0% in P-II. The cumulative distribution of NHAQI-based population-weighted exposure revealed that 24% of the total population was still exposed to light pollution in spring during P-II, while the air quality in other three seasons had significant improvements and all people were under healthy air quality level.

IoT-based monitoring system and air quality prediction using machine learning for a healthy environment in Cameroon

This paper is aimed at developing an air quality monitoring system using machine learning (ML), Internet of Things (IoT), and other elements to predict the level of particulate matter and gases in the air based on the air quality index (AQI). It is an air quality assessor and therefore a means of achieving the Sustainable Development Goals (SDGs), in particular, SDG 3.9 (substantial reduction of the health impacts of hazardous substances) and SDG 11.6 (reduction of negative impacts on cities and populations). AQI quantifies and informs the public about air pollutants and their adverse effects on public health. The proposed air quality monitoring device is low-cost and operates in real-time. It consists of a hardware unit that detects various pollutants to assess air quality as well as other airborne particles such as carbon dioxide (CO2), methane (CH4), volatile organic compounds (VOCs), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter with an aerodynamic diameter of 2.5 microns or less (PM2.5). To predict air quality, the device was deployed from November 1, 2022, to February 4, 2023, in certain bauxite-rich areas of Adamawa and certain volcanic sites in western Cameroon. Therefore, machine learning algorithm models, namely, multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), XGBoost (XGB), and K-nearest neighbors (KNN) were applied to analyze the collected concentrations and predict the future state of air quality. The performance of these models was evaluated using mean absolute error (MAE), coefficient of determination (R-square), and root mean square error (RMSE). The obtained data in this study show that these pollutants are present in selected localities albeit to different extents. Moreover, the AQI values obtained range from 10 to 530, with a mean of 132.380 ± 63.705, corresponding to moderate air quality state but may induce an adverse effect on sensitive members of the population. This study revealed that XGB regression performed better in air quality forecasting with the highest R-squared (test score of 0.9991 and train score of 0.9999) and lowest RMSE (test score of 1.5748 and train score of 0. 0073) and MAE (test score of 0.0872 and train score of 0.0020), while the KNN model had the worst prediction (lowest R-squared and highest RMSE and MAE). This embryonic work is a prototype for projects in Cameroon as measurements are underway for a national spread over a longer period of time.

The Diwali festival: short-term high effect of fireworks emissions on particulates and their associated empirically calculated health risk assessment at Bhubaneswar city

This article elucidates the role of the short-term combustion of firecrackers and sparklers as a significant source of atmospheric pollutants that deteriorate ambient air quality and increase health risks during the popular Diwali festival. The study was conducted at Bhubaneswar during the festive celebration in early November 2021 (4th Nov) and late October 2022 (24th Oct) to assess the level of particulates (PM2.5 and PM10 mass concentration) and the relative health risks associated with them. PM2.5 (113.83 µg/m3) and PM10 (204.32 µg/m3) showed significant rises on D-day at all seven different sites that exceeded the NAAQS in 2021. From 2021 to 2022, an overall decrease in PM2.5 (41%) and PM10 (36%) was observed. On D-day, the total concentration of quantified metals in PM2.5 and PM10 were found to be 4.83 µg/m3 5.97 µg/m3 (2021) and 5.08 µg/m3 5.18 µg/m3 (2022) respectively. The AQI during both years (2021-2022) was found to be high for PM2.5 (unhealthy) and PM10 (moderate), but it was markedly good for all other pollutants on the scale. The overall population in the study area were under a significant health risk was observed in the overall population as PM surpassed the threshold concentration amid the festivities for consecutive years, with PM2.5 being more potent than PM10. The total excess health risk in 2022 was found to be decreased lower by ~ 88% from 2021 on D-day. But, metal exposure (through inhalation) in children were more compared to the adults for both the years. However, the exposure risk of both children and adults were high in the year 2022 with inhalation of metals like K, Al, Ba, Fe and Ca found in higher concentration and directly emitted from the firecrackers.

Indoor environmental conditions of selected shopping malls in Nigeria: A comparative study of microclimatic conditions, noise levels, and microbial burdens

The activities of people and equipment used within shopping malls are major factors that contribute to air pollution and increased sound levels, thereby affecting indoor environmental quality and the well-being of mall operators. This study assessed indoor environmental quality through microbial characterization and measurement of environmental conditions present in selected shopping malls. Investigations were conducted at three shopping malls in Ibadan selected through convenience sampling technique. Environmental parameters such as noise level, relative humidity, temperature, PM2.5 levels, total volatile organic compound (TVOC) levels, microbial characterization, and quantity were determined. Microclimatic parameters (temperature and relative humidity) were measured using a 4-in-1 Precision Gold N09AQ multi-tester. Culturable airborne microbes were collected using the settle plate technique. PM2.5 and TVOC levels were measured using a Thermo Scientific MIE pDR-1500 PM monitor and sf200-TVOC meter respectively. Two bacteria species and five fungi species were isolated across the malls. The noise levels ranged from 61.27 to 81.20 dB. The mean temperatures (highest mean of 33.44 ± 1.42 °C), PM2.5 (highest mean of 114.06 ± 25.64 μg/m3), and TVOC (highest mean of 55.21 ± 8.28 ppm) concentrations were higher than the permissible limits stipulated by the WHO guidelines and NESREA standard limits across all the selected malls. A positive correlation was found to exist between particulate matter and TVOC (r = 0.174, p = 0.004). The total bacteria count was generally high with the highest mean of 1965.33 ± 368.56 CFU/m3, while the total fungi count was generally low with the highest mean of 579.82 ± 51.55 CFU/m3. Bacillus spp. and Candida spp. were found to the consistent from all sample points across the three malls. The bacteria isolated are Gram-positive bacteria associated with human skin which suggests a high rate of indoor pollution from humans. In conclusion, this research has demonstrated the necessity to monitor noise levels and indoor air quality in malls. Also, there is need for government policies to improve indoor air quality which must be enforced and regulated, especially within shopping malls.

Characteristics of ambient air quality and its air quality index (AQI) model in Shanghai, China

Long-term observations indicate that, the ambient air quality in Shanghai continues to improve, however the synergistic effects between the air pollutants PM2.5, O3 and NO2 are also increasing. The concentration of chemical components included in PM2.5 is higher in moderately polluted air containing multiple pollutants. This suggests that air pollution metrics based on multi-pollutant synergy are more descriptive of ambient air quality than single-pollutant air quality index (AQI) models that may ignore the effect of synergy between pollutants on ambient air quality forecasts. Therefore, this study proposes a new multi-pollutant air quality index model (NMAQI) based on four air pollutants (PM2.5, SO2, NO2 and O3) that emphasizes the relationship between PM2.5, NO2 and O3 in ambient air. The model successfully categorized observational data into classes of good, moderate, and polluted air quality ratings. Verification of the NMAQI model using the PM2.5 chemical composition spectrum shows that the NMAQI model can more accurately classify samples with high concentrations of chemical components (often misclassified by AQI) into high pollution levels. The model has an improved capacity to assess the degree of pollution in urban ambient air and to reduce the risk of public exposure to highly polluted atmospheric environments.

Spatiotemporal characteristics of air pollutants and their associated health risks in '2+26' cities in China during 2016-2020 heating seasons

To understand characteristics of air pollutants and their associated health risks in recent heating seasons in China, ambient monitoring data of six air pollutants in '2 + 26' cities in Beijing-Tianjin-Hebei and its surrounding areas (known as the BTH2+26 cities) during 2016-2020 heating seasons was analyzed. Results show that daily average concentrations of PM2.5, PM10, SO2, NO2, and CO dropped significantly in BTH2+26 cities from the 2016-2017 heating season to 2019-2020 heating season, while 8h O3 increased markedly. During 2016-2020 heating seasons, annual average values of total excess risks (ERtotal) were 2.3% mainly contributed by PM2.5 (54.4%) and PM10 (36.1%). With PM2.5 pollution worsening, PM10 and NO2 were the important contribution factors of the enhanced ERtotal. Higher health-risk based air quality index (HAQI) values were mainly concentrated in the western Hebei and northern Henan. HAQI showed spatial agglomeration effect in four heating seasons. Impact factors of HAQI varied in different heating seasons. These findings can provide useful insights for China to further propose effective control strategies to alleviate air pollution in the future.

Air quality health index (AQHI) based on multiple air pollutants and mortality risks in Taiwan: Construction and validation

The currently used air quality index (AQI) is not able to capture the additive effects of air pollution on health risks and reflect non-threshold concentration-response relationships, which has been criticized. We proposed the air quality health index (AQHI) based on daily air pollution-mortality associations, and compared its validity in predicting daily mortality and morbidity risks with the existing AQI. We examined the excess risk (ER) of daily elderly (≥65-year-old) mortality associated with 6 air pollutants (PM2.5, PM10, SO2, CO, NO2, and O3) in 72 townships across Taiwan from 2006 to 2014 by performing a time-series analysis using a Poisson regression model. Random effect meta-analysis was used to pool the township-specified ER for each air pollutant in the overall and seasonal scenarios. The integrated ERs for mortality were calculated and used to construct the AQHI. The association of the AQHI with daily mortality and morbidity were compared by calculating the percentage change per interquartile range (IQR) increase in the indices. The magnitude of the ER on the concentration-response curve was used to evaluate the performance of the AQHI and AQI, regarding specific health outcomes. Sensitivity analysis was conducted using coefficients from the single- and two-pollutant models. The coefficients of PM2.5, NO2, SO2, and O3 associated with mortality were included to form the overall and season-specific AQHI. An IQR increase in the overall AQHI at lag 0 was associated with 1.90%, 2.96%, and 2.68% increases in mortality, asthma, and respiratory outpatient visits, respectively. The AQHI had higher ERs for mortality and morbidity on the validity examinations than the current AQI. The AQHI, which captures the combined effects of air pollution, can serve as a health risk communication tool to the public.

Machine learning assesses drivers of PM2.5 air pollution trend in the Tibetan Plateau from 2015 to 2022

The Tibetan Plateau (known as the Earth's Third Pole) has significant impact on climate. Fine particulate matter (PM_2.5) is an important air pollutant in this region and has significant impact on health and climate. To mitigate PM_2.5 air pollution over China, a series of clean air actions has been implemented. However, interannual trends in particulate air pollution and its response to anthropogenic emissions in the Tibetan Plateau are poorly understood. Here, we applied a random forest (RF) algorithm to quantify drivers of PM_2.5 trends in six cities of the Tibetan Plateau from 2015 to 2022. The decreasing trends (-5.31 to -0.73 μg m^-3 a^-1) in PM_2.5 during 2015-2022 were observed in all cities. The RF weather-normalized PM_2.5 trends - which were driven by anthropogenic emissions - were -4.19 to -0.56 μg m^-3 a^-1, resulting in dominant contributions (65 %-83 %) to the observed PM_2.5 trends. Relative to 2015, such anthropogenic emission driver was estimated to contribute -27.12 to -3.16 μg m^-3 to declines in PM_2.5 concentrations in 2022. However, the interannual changes in meteorological conditions only made a small contribution to the trends in PM_2.5 concentrations. Potential source analysis suggested biomass burning from local residential sector and/or long-range transports originated from South Asia could significantly promote PM_2.5 air pollution in this region. Based on health-risk air quality index (HAQI) assessment, the HAQI value was decreased by 15 %-76 % between 2015 and 2022 in these cities, with significant contributions (47 %-93 %) from anthropogenic emission abatements. Indeed, relative contribution of PM_2.5 to the HAQI was decreased from 16 %-30 % to 11 %-18 %, while increasing and significant contribution from ozone was observed, highlighting that further effective mitigation of both PM_2.5 and ozone air pollution could obtain more substantial health benefits in the Tibetan Plateau.

Spatiotemporal evolution characteristics and prediction analysis of urban air quality in China

To describe the spatiotemporal variations characteristics and future trends of urban air quality in China, this study evaluates the spatiotemporal evolution features and linkages between the air quality index (AQI) and six primary pollution indicators, using air quality monitoring data from 2014 to 2022. Seasonal autoregressive integrated moving average (SARIMA) and random forest (RF) models are created to forecast air quality. (1) The study's findings indicate that pollution levels and air quality index values in Chinese cities decline annually, following a "U"-shaped pattern with a monthly variation. The pollutant levels are high in winter and low in spring, and low in summer and rising in the fall (O₃ shows the opposite). (2) The spatial distribution of air quality in Chinese cities is low in the southeast and high in the northwest, and low in the coastal areas and higher in the inland areas. The correlation coefficients between AQI and the pollutant concentrations are as follows: fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ozone (O₃) values are correlated at 0.89, 0.84, 0.54, 0.54, 0.32, and 0.056, respectively. (3) In terms of short-term AQI predictions, the RF model performs better than the SARIMA model. The long-term forecast indicates that the average AQI value in Chinese cities is expected to decrease by 0.32 points in 2032 compared to the 2022 level of 52.95. This study has some guiding significance for the analysis and prediction of urban air quality.

Machine Learning Explains Long-Term Trend and Health Risk of Air Pollution during 2015-2022 in a Coastal City in Eastern China

Exposure to air pollution is one of the greatest environmental risks for human health. Air pollution level is significantly driven by anthropogenic emissions and meteorological conditions. To protect people from air pollutants, China has implemented clean air actions to reduce anthropogenic emissions, which has led to rapid improvement in air quality over China. Here, we evaluated the impact of anthropogenic emissions and meteorological conditions on trends in air pollutants in a coastal city (Lianyungang) in eastern China from 2015 to 2022 based on a random forest model. The annual mean concentration of observed air pollutants, including fine particles, inhalable particles, sulfur dioxide, nitrogen dioxide, and carbon monoxide, presented significant decreasing trends during 2015-2022, with dominant contributions (55-75%) by anthropogenic emission reduction. An increasing trend in ozone was observed with an important contribution (28%) by anthropogenic emissions. The impact of meteorological conditions on air pollution showed significant seasonality. For instance, the negative impact on aerosol pollution occurred during cold months, while the positive impact was in warm months. Health-risk-based air quality decreased by approximately 40% in 8 years, for which anthropogenic emission made a major contribution (93%).

A new approach of air pollution regionalization based on geographically weighted variations for multi-pollutants in China

Air pollution regionalization is a key and necessary action to identify pollution regions for implementing control measures. Here we present a new approach called Geographically Weighted Rotation Empirical Orthogonal Function (GWREOF) for air pollution regionalization in China. Compared with previous methods, such as EOF, REOF, and K-mean, GWREOF better accounts for the variability of air pollution conditions driven by emission patterns and meteorology with centralized spatial locations. We apply GWREOF to multiple air pollutants (such as PM_2.5, O₃, and other monitored air pollutants) and air quality metrics using their measured spatial and temporal variations in 337 Chinese cities over 2015-2020. We find that the regionalization results for different air pollutants are highly similar, primarily determined by topography and meteorological conditions in China. Therefore, we propose an integrated regionalization result, which identifies 18 air pollution control regions in China and can be applied to multiple pollutants and different years. We further analyze PM_2.5, O₃, and O_X (O₃ + NO₂) pollution levels and their correlations in these regions. PM_2.5 and O₃ correlations are generally strongly positive in southern China while negative in northern China. However, PM_2.5 and O_X correlations are broadly positive in China, reflecting the crucial role of atmospheric oxidizing capacity. Regional-specific and coordinated control measures are in need as China's air pollution strategy transits from PM_2.5-focused to PM_2.5-O₃ synergic control.

Air pollution and its associated health risks before and after COVID-19 in Shaanxi Province, China

Air pollution is a serious environmental problem that damages public health. In the present study, we used the segmentation function to improve the health risk-based air quality index (HAQI) and named it new HAQI (NHAQI). To investigate the spatiotemporal distribution characteristics of air pollutants and the associated health risks in Shaanxi Province before (Period I, 2015-2019) and after (Period II, 2020-2021) COVID-19. The six criteria pollutants were analyzed between January 1, 2015, and December 31, 2021, using the air quality index (AQI), aggregate AQI (AAQI), and NHAQI. The results showed that compared with AAQI and NHAQI, AQI underestimated the combined effects of multiple pollutants. The average concentrations of the six criteria pollutants were lower in Period II than in Period I due to reductions in anthropogenic emissions, with the concentrations of PM_2.5 (particulate matter ≤2.5 μm diameter), PM₁₀ (PM ≤ 10 μm diameter) SO₂, NO₂, O₃, and CO decreased by 23.5%, 22.5%, 45.7%, 17.6%, 2.9%, and 41.6%, respectively. In Period II, the excess risk and the number of air pollution-related deaths decreased considerably by 46.5% and 49%, respectively. The cumulative population distribution estimated using the NHAQI revealed that 61% of the total number of individuals in Shaanxi Province were exposed to unhealthy air during Period I, whereas this proportion decreased to 16% during Period II. Although overall air quality exhibited substantial improvements, the associated health risks in winter remained high.

Short-Term Effect of Moderate Level Air Pollution on Outpatient Visits for Multiple Clinic Departments: A Time-Series Analysis in Xi'an China

There is limited evidence concerning the association between air pollution and different outpatient visits in moderately polluted areas. This paper investigates the effects of moderate-level air pollution on outpatient visits associated with six categories of clinic department. We analyzed a total of 1,340,791 outpatient visits for the pediatric, respiratory, ear-nose-throat (ENT), cardiovascular, ophthalmology, and orthopedics departments from January 2016 to December 2018. A distributed lag nonlinear model was used to analyze the associations and was fitted and stratified by age and season (central heating season and nonheating season). We found SO2 had the largest effect on pediatrics visits (RR = 1.105 (95%CI: 1.090, 1.121)). Meanwhile, PM2.5 and SO2 had greater effects on ENT visits for people under 50 years old. The results showed a strong association between O3 and cardiovascular outpatient visits in the nonheating season (RR = 1.273, 95% CI: 1.189,1.358). The results showed every 10 μg/m3 increase in SO2 was associated with a lower number of respiratory outpatient visits. Significant different associations were observed in PM2.5, NO2, CO, and O3 on ophthalmology visits between the heating and nonheating seasons. Although no significant association has been found in existing studies, our findings showed PM2.5 and NO2 were significantly related to orthopedic outpatient visits for people under 60 (RR = 1.063 (95%CI: 1.032, 1.095), RR = 1.055 (95%CI: 1.011, 1.101)). This study also found that the effect-level concentrations of air pollutants for some clinic departments were lower than the national standards, which means that people should also pay more attention when the air quality is normal.

Characteristics, health risks, and premature mortality attributable to ambient air pollutants in four functional areas in Jining, China

Air pollution is one of the leading causes for global deaths and understanding pollutant emission sources is key to successful mitigation policies. Air quality data in the urban, suburban, industrial, and rural areas (UA, SA, IA, and RA) of Jining, Shandong Province in China, were collected to compare the characteristics and associated health risks. The average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, and CO show differences of -3.87, -16.67, -19.24, -15.74, and -8.37% between 2017 and 2018. On the contrary, O₃ concentrations increased by 4.50%. The four functional areas exhibited the same seasonal variations and diurnal patterns in air pollutants, with the highest exposure excess risks (ERs) resulting from O₃. More frequent ER days occurred within the 25-30°C, but much larger ERs are found within the 0-5°C temperature range, attributed to higher O₃ pollution in summer and more severe PM pollution in winter. The premature deaths attributable to six air pollutants can be calculated in 2017 and 2018, respectively. Investigations on the potential source show that the ER of O₃ (r of 0.86) had the tightest association with the total ER. The bivariate polar plots indicated that the highest health-based air quality index (HAQI) in IA influences the HAQI in UA and SA by pollution transport, and thus can be regarded as the major pollutant emission source in Jining. The above results indicate that urgent measures should be taken to reduce O₃ pollution taking into account the characteristics of the prevalent ozone formation regime, especially in IA in Jining.

Increased emergency cases for out-of-hospital cardiac arrest due to cold spells in Shenzhen, China

Cold spells have been associated with specific diseases. However, there is insufficient scientific evidence on the effects of cold spells on out-of-hospital cardiac arrest (OHCA). Data on OHCA cases and on meteorological factors and air pollutants were collected between 2013 and 2020. We adopted a quasi-Poisson generalized additive model with a distributed lag nonlinear model (DLNM) to estimate the effect of cold spells on daily OHCA incidence. Backward attributable risk within the DLNM framework was calculated to quantify the disease burden. We compared the effects and OHCA burden of cold spells using nine definitions. The risks of different cold spells on OHCA increased at higher intensities and longer durations. Based on Akaike's information criterion for the quasi-Poisson regression model and the attributable risk, the optimal cold spell was defined as a period in the cold month when the daily mean temperature was below the 10th percentile of the temperature distribution in the study period for at least 2 days. The single-day effect of the optimal cold spell on OHCA occurred immediately and lasted for approximately 1 week. The maximum single-day effect was 1.052 (95% CI: 1.018-1.087) at lag0, while the maximum cumulative effect was 1.433 (95% CI:1.148-1.788) after a 14-day lag. Men were more susceptible to cold spells. Young and middle-aged people were affected by cold spells similar to the elderly. Cold spells can increase the risk of OHCA with an approximately 1-week lag effect. Health regulators should take more targeted measures to protect susceptible populations during cold weather.

Investigation of a haze-to-dust and dust swing process at a coastal city in northern China part I: Chemical composition and contributions of anthropogenic and natural sources

The long retention of dust air masses in polluted areas, especially in winter, may efficiently change the physicochemical properties of aerosols, causing additional health and ecological effects. A large-scale haze-to-dust weather event occurred in the North China Plain (NCP) region during the autumn-to-winter transition period in 2018, affecting the coastal city Qingdao several times between Nov. 27th and Dec. 1st. To study the evolution of the pollution process, we analyzed the chemical characteristics of PM_2.5 and PM_10-2.5 and source apportionments of PM_2.5 and PM_10, The dust stagnated around NCP and moved out and back to the site, noted as dust swing process, promoting SO₄^2- formation in PM_2.5 and NO₃^- formation in PM_10-2.5. Source apportionments were analyzed using the Positive Matrix Factorization (PMF) receptor model and weighted potential source contribution function (WPSCF). Before the dust invasion, Qingdao was influenced by severe haze; waste incineration and coal burning were the major contributors (~80 %) to PM_2.5, and the source region was in the southwest of Shandong Province. During the initial dust event, mineral dust and the mixed factor of dust and sea salt were the major contributors (46.0 % of PM_2.5 and 86.5 % of PM₁₀). During the polluted dust period, the contributions of regional transported biomass burning (22.3 %), vehicle emissions (20.8 %), and secondary aerosols (33.8 %) to PM_2.5 from the Beijing-Tianjin-Hebei region significantly increased. The secondary aerosols source was more regional than that of vehicle emissions and biomass burning and contributed considerably to PM₁₀ (30.8 %) during the dust swing process. Our findings demonstrate that environmental managers should consider the possible adverse effects of winter dust on regional and local pollution.

The establishment of Air Quality Health Index in China: A comparative analysis of methodological approaches

BACKGROUND

The Air Quality Index (AQI) has been criticized because it does not adequately account for the health effect of multi-pollutants. Although the developed Air Quality Health Index (AQHI) is a more effective communication tool, little is known about the best method to construct AQHI on long time and large spatial scales.

OBJECTIVES

To further evaluate the validity of existing approaches to the establishment of AQHI on both long time and larger spatial scales.

METHODS

By introducing 3 approaches addressing multi-pollutant exposures: cumulative risk index (CRI), supervised principal component analysis (SPCA), and Bayesian multi-pollutants weighted model (BMP), we constructed CRI-AQHI, SPCA-AQHI, BMP-AQHI and standard-AQHI on cardiovascular mortality in China from 2015 to 2019 at both the national and geographic regional levels. We further assessed the performance of the four methods in estimating the joint effect of multi-pollutants by simulations under various scenarios of pollution effect.

RESULTS

The results of national China showed that the BMP-AQHI improved the goodness of fit of the standard-AQHI by 108.24%, followed by CRI-AQHI (5.02%), and all AQHIs performed better than AQI, consistent with 6 geographic regional results. In addition, the simulation result showed that the BMP method provided stable and relatively accurate estimations of the short-term combined effect of exposure to multi-pollutants.

CONCLUSIONS

AQHI based on BMP could communicate the air pollution risk to the public more effectively than the current AQHI and AQI.

Spatial and temporal characteristics of air pollutants and their health effects in China during 2019-2020

This work presents the temporal and spatial characteristics of the major air pollutants and their associated health risks in China from 2019 to 2020, by using the monitoring data from 367 cities. The annual average PM_2.5, PM₁₀, NO₂, SO₂, CO, and O₃ concentrations decreased by 10.9%, 13.2%, 9.3%, 10.1%, 9.4%, and 5.5% from 2019 to 2020. National average PM_2.5 concentration in 2020 met the standard of 35 μg/m³, and that of O₃ decreased from 2019. COVID-19 lockdown affected NO₂ level dramatically, yet influences on PM_2.5 and O₃ were less clear-cut. Positive correlations between PM_2.5 and O₃ were found, even in winter in all five key regions, e.g., Jing-Jin-Ji (JJJ), FenWei Plain (FWP), Yangtze River Delta (YRD), Pearl River Delta (PRD) and Chengdu-Chongqing Region (CCR), indicating importance of secondary production for both PM_2.5 and O₃. Large seasonal variability of PM_2.5-SO₂ correlation indicates a varying role of SO₂ to PM_2.5 pollution in different seasons; and generally weak correlations in winter between PM_2.5 and NO₂ or SO₂ reveal the complexity of secondary formation processes to PM_2.5 pollution in winter. Multilinear regression analysis between PM_2.5 and SO₂, NO₂ and CO demonstrates that PM_2.5 is more sensitive to the change of NO₂ than SO₂ in JJJ, FWP, PRD and CCR, suggesting a priority of NO_x emission control for future PM_2.5 reduction. Furthermore, the new World Health Organization Air Quality Guidelines (WHO AQG2021) were adopted to calculate the excess health risks (ER) as well as the health-risk based air quality index (HAQI_WHO) of the pollutants. Such assessment points out the severity of air pollution associated health risks under strict standards: 40.0% of days had HAQI_WHO>100, while only 14.4% days had AQI>100. PM_2.5 ER was generally larger than O₃ ER, but O₃ ER in low PM_2.5 region (PRD) and during summer became more serious. Notably, NO₂ ER became even more important than PM_2.5 due to its strict limit of WHO AQG2021. Overall, our results highlight the increasing importance of O₃ in both air quality evaluation and health risk assessment, and the importance of coordinated mitigation of multiple pollutants (mainly PM_2.5, O₃ and NO₂) in protecting the public health.

Evaluating the real changes of air quality due to clean air actions using a machine learning technique: Results from 12 Chinese mega-cities during 2013-2020

China has implemented two national clean air actions in 2013-2017 and 2018-2020, respectively, with the aim of reducing primary emissions and hence improving air quality at a national level. It is important to examine the effectiveness of such emission reductions and assess the resulting changes in air quality. However, such evaluation is difficult as meteorological factors can amplify, or obscure the changes of air pollutants, in addition to the emission reduction. In this study, we applied the random forest machine learning technique to decouple meteorological influences from emissions changes, and examined the deweathered trends of air pollutants in 12 Chinese mega-cities during 2013-2020. The observed concentrations of all criteria pollutants except O₃ showed significant declines from 2013 to 2020, with PM_2.5 annual decline rates of 6-9% in most cities. In contrast, O₃ concentrations increased with annual growth rates of 1-9%. Compared with the observed results, all the pollutants showed smoothed but similar variation in trend and annual rate-of-change after weather normalization. The response of O₃ to NO₂ concentrations indicated significant regional differences in photochemical regimes, and the differences between observed and deweathered results provided implications for volatile organic compound emission reductions in O₃ pollution mitigation. We further evaluated the effectiveness of first and second clean air actions by removing the meteorological influence. We found that the meteorology can make negative or positive contribution in reducing pollutant concentrations from emission reduction, depending on type of pollutants, locations, and time period. Among the 12 mega-cities, only Beijing showed a positive meteorological contribution in amplifying reductions in main pollutants except O₃ during both clean air action periods. Considering the large and variable impact of meteorological effects in changing air quality, we suggest that similar deweathered analysis is needed as a routine policy evaluation tool on a regional basis.

Spatial-Temporal Distribution and Variation of NO2 and Its Sources and Chemical Sinks in Shanxi Province, China

In present China, continuing to control PM2.5 (particulate matter < 2.5 μm) and preventing the rise of O3 are the most urgent environmental tasks in its air clean actions. Considering that NO2 is an important precursor of PM2.5 and O3, a comprehensive analysis around this pollutant was conducted based on the real-time-monitoring data from Jan 2018 to Mar 2019 in 11 prefecture-level cities in Shanxi Province of China. The results showed that the annual average concentration of NO2 in Shanxi prefecture-level cities is mainly distributed in the range of 28.84–48.93 μg/m3 with the values in five cities exceeding the Chinese Grade Ⅱ standard limit (40 μg/m3). The over-standard days were all concentrated in the heating season with a large pollution peak occurring in winter except in Lvliang, while four cities also had a small pollution peak in summer. High NO2 polluted areas were mainly concentrated in the central part of Shanxi, and trended on the whole from the southwest to the northeast (Lvliang/Linfen—Taiyuan/Jinzhong—Yangquan/Jinzhong), which was different from the spatial distribution of PM2.5 and O3. Lvliang was the hot spot of NO2 pollution in summer, while Taiyuan was the hot spot in winter. Concentration Weighted Trajectory (CWT) analysis indicated that central-north Shaanxi, central-south Shanxi, northern Henan, the south of Shijiazhuang and areas around Erdos in Inner Mongolia were important source areas of NO2 in Shanxi besides local emissions. Our findings are expected to provide valuable implications to policymakers in Shanxi of China to effectively abate the air pollution.

The Coordinated Development and Regulation Research on Public Health, Ecological Environment and Economic Development: Evidence from the Yellow River Basin of China

The dual problems of the public crisis from the global epidemic and the deterioration of the ecological environment constrain the economic development in the Yellow River Basin. To promote the sustainable and balanced development in the Yellow River Basin, this paper takes public health, ecological environment, and economic development, as a whole, to study the coordinated development of the Yellow River Basin. Based on coupling coordinated theory, we use the SMI-P method to evaluate the coordinated development index of public health, the ecological environment, and economic development in the Yellow River Basin. Moreover, we use the coordinated regulation and obstacle factor diagnosis to identify the main influencing factors and design regulation methods to optimize the coordinated development index. The results found that (1), during the research period, there is spatiotemporal heterogeneity in the coordinated development level in the Yellow River Basin. From 2009 to 2019, the overall development index increased steadily, while the regional disparity in the coordinated development level was obvious. (2) The ecological environment indicators contribute more to the relevance and obstacle factors, such as the average concentration of fine particulate matter, per capita arable land area, afforestation area, etc. (3) After regulating the overall development level of the Yellow River Basin, we prove that Path 4, which comprehensively considers the relevance and obstacle factors, performs better.

Coordinated health effects attributable to particulate matter and other pollutants exposures in the North China Plain

Hebei Province, located in the North China Plain (NCP) and encircling Beijing and Tianjin, has been suffering from severe air pollution. The monthly average fine particulate matter (PM_2.5) concentration was up to 276 μg/m³ in Hebei Province, which adversely affects human health. However, few studies evaluated the coordinated health impact of exposure to PM (PM_2.5 and PM₁₀) and other key air pollutants (SO₂, NO₂, CO, and surface ozone (O₃)). In this study, we systematically analyzed the health risks (both mortality and morbidity) due to multiple air pollutants exposures in Hebei Province. The economic loss associated with these health consequences was estimated using the value of statistical life (VSL) and cost of illness (COI) methods. Our results show the health burden and economic loss attributable to multiple ambient air pollutants exposures in Hebei Province is substantial. In 2017, the total premature mortality from multiple air pollutants exposures in Hebei Province was 69,833 (95% CI: 55,549-83,028), which was 2.9 times higher than that of the Pearl River Delta region (PRD). Most of the potential economic loss (79.65%) was attributable to premature mortality from air pollution. The total economic loss due to the health consequences of multiple air pollutants exposures was 175.16 (95% CI: 134.61-224.61) billion Chinese Yuan (CNY), which was 4.92% of Hebei Province's annual gross domestic product (GDP). Thus, the adverse health effects and economic loss caused by exposure to multiple air pollutants should be seriously taken into consideration. To alleviate these damages, Hebei's government ought to establish more stringent measures and regulations to better control air pollution.

Air pollutant spatiotemporal evolution characteristics and effects on human health in North China

North China, the political, economic, and cultural center of China, has been greatly harmed by frequent air pollution incidents. Therefore, it is vital to study air pollution characteristics and clarify their impact on human health. In this study, we first analyzed the spatiotemporal variations of air pollutants (PM_2.5, PM₁₀, CO, SO₂, NO₂, and O₃) in North China from 2016 to 2019. Then, the air quality index (AQI), aggregate air quality index (AAQI), and health risk based air quality index (HAQI) were used to assess health risks. Based on these, the AirQ_2.2.3 model was used to quantify health effects. The results showed that the major pollutant in the cities surrounding Beijing was PM_2.5, while PM₁₀ dominated in distant cities. Annual concentrations decreased (except for O₃), which is related to governmental emission reduction policies. However, O₃ concentrations increased owing to the complex precursor emissions. The AQI underestimated air pollution, while the AAQI and HAQI were accurate; the latter indicated that 55% of the study region population was exposed to polluted air. The AirQ_2.2.3 model quantified the total mortality proportions attributable to PM_2.5, PM₁₀, SO₂, CO, NO₂, and O₃, which were 1.87%, 3.12%, 1.11%, 1.40%, 4.19%, and 2.52%, respectively. In high concentrations, PM₁₀ and PM_2.5 pose significant health risks. The health effects of SO₂, NO₂, CO, and O₃ at lower concentrations were more obvious, indicating that the expected mortality rate due to low concentrations of some pollutants was much higher than that due to high concentrations of other pollutants.

A critical evaluation of air quality index models (1960-2021)

The formulation of an adequate and practical Atmospheric Air Quality Management Plan at different spatial scales at local (micro), city (medium), national (macro)), and temporal (short and long term) is an indispensable solution to prevent the public from air pollution health risk. The air quality monitoring system provides regulatory agencies a comprehensive data of current air contaminants in a particular location. Then, air monitoring data of pollutants is processed into a dimensionless unit called the "Air Quality Index" (AQI); it serves as an information medium for the people to know the air quality health of their location and takes preventative steps accordingly (public participation). Thus, the AQI is a beneficial tool for the public, stakeholders, and regulators to understand the current state of air quality. AQI across the globe considers the number of pollutants (most of the developed countries and some developing countries considers PM_2.5 to measure the overall status of air quality being monitored), averaging time for which pollutants are measured, calculation method to compute air quality indices for each pollutant, calculation mode to aggregate the overall index, scale of an index, categories, colour coding scheme, and related descriptive terms of the pollutants. This article presents rationalized and extensive reviews of various Air Quality Index (AQI) models utilized worldwide from 1960 to 2021, comparing them based on several parameters such as types and number of pollutants (criteria or hazardous air pollutants), averaging time (long-term or short-term), calculation methods (linear or nonlinear), calculation modes [single-pollutant (maximum value) or multi-pollutants (combined effect)]. By analysing the strengths and flaws of all the AQI models developed so far, it is recommended to develop a more reliable, extensible, and comparable AQI model to be employed as an executive tool for designing strategic pollution abatement programs to preserve public health.

Potency of the pandemic on air quality: An urban resilience perspective

Since the outbreak of COVID-19 pandemic, the lockdown policy across the globe has brought improved air quality while fighting against the coronavirus. After the closure, urban air quality was subject to emission reduction of air pollutants and rebounded to the previous level after the potency period of recession. Different response patterns exhibit divergent sensitivities of urban resilience in regard to air pollution. In this paper, we investigate the post-lockdown AQI values of 314 major cities in China to analyse their differential effects on the influence factors of urban resilience. The major findings of this paper include: 1) Cities exhibit considerable range of resilience with their AQI values which are dropped by 21.1% per day, took 3.97 days on average to reach the significantly decreased trough point, and reduced by 49.3% after the lockdown initiatives. 2) Mega cities and cities that locate as the focal points of transportation for nearby provinces, together with those with high AQI values, were more struggling to maintain a good air quality with high rebounds. 3) Urban resilience shows divergent spatial sensitivities to air pollution controls. Failing to consider multi-dimensional factors besides from geomorphological and economical activities could lead to uneven results of environmental policies. The results unveil key drivers of urban air pollution mitigation, and provide valuable insights for prediction of air quality in response to anthropogenic interference events under different macro-economic contexts. Research findings in this paper can be adopted for prevention and management of public health risks from the perspective of urban resilience and environmental management in face of disruptive outbreak events in future.

Disentangling drivers of air pollutant and health risk changes during the COVID-19 lockdown in China

The COVID-19 restrictions in 2020 have led to distinct variations in NO₂ and O₃ concentrations in China. Here, the different drivers of anthropogenic emission changes, including the effects of the Chinese New Year (CNY), China's 2018-2020 Clean Air Plan (CAP), and the COVID-19 lockdown and their impact on NO₂ and O₃ are isolated by using a combined model-measurement approach. In addition, the contribution of prevailing meteorological conditions to the concentration changes was evaluated by applying a machine-learning method. The resulting impact on the multi-pollutant Health-based Air Quality Index (HAQI) is quantified. The results show that the CNY reduces NO₂ concentrations on average by 26.7% each year, while the COVID-lockdown measures have led to an additional 11.6% reduction in 2020, and the CAP over 2018-2020 to a reduction in NO₂ by 15.7%. On the other hand, meteorological conditions from 23 January to March 7, 2020 led to increase in NO₂ of 7.8%. Neglecting the CAP and meteorological drivers thus leads to an overestimate and underestimate of the effect of the COVID-lockdown on NO₂ reductions, respectively. For O₃ the opposite behavior is found, with changes of +23.3%, +21.0%, +4.9%, and -0.9% for CNY, COVID-lockdown, CAP, and meteorology effects, respectively. The total effects of these drivers show a drastic reduction in multi-air pollutant-related health risk across China, with meteorology affecting particularly the Northeast of China adversely. Importantly, the CAP's contribution highlights the effectiveness of the Chinese government's air-quality regulations on NO₂ reduction.

Fine particulate matter exposure in four transport modes of Greater Cairo

The number of daily commuters in Greater Cairo has exceeded 15 million nevertheless personal exposure studies in transport microenvironments are limited. The aim of this study is to quantify PM_2.5 exposure during peak hours in four transport modes of Greater Cairo - car (windows-open, windows-closed with recirculation and AC-on), microbus (windows-open), cycling and walking - and understand its underlying drivers. Data was collected using a pDR-1500 monitor and analysed to capture concentration variations, spatial variability, exposure doses, commuting costs versus inhaled doses, health burden and economic losses. Car with recirculation resulted in the least average PM_2.5 concentrations (32 ± 6 μg/m³), followed by walking (77 ± 35 μg/m³), car with windows-open (82 ± 32 μg/m³), microbus with windows-open (96 ± 29 μg/m³) and cycling (100 ± 28 μg/m³). Evening hours observed average PM_2.5 concentrations by 26-58% lesser than morning. Spatial variability analysis showed that 75th-90th percentile PM_2.5 concentrations coincided with congested spots. Cycling and walking lanes are rare hence commuters are exposed to surges in PM_2.5 concentrations when passing near construction and solid waste burning sites. Cycling and walking also resulted in inhaling 40-times and 32-times higher PM_2.5 dose per kilometre than for car with recirculation. Commuting by microbus cost (with windows-open) ~45% of car cost (with recirculation) but it resulted in 4-times higher inhaled PM_2.5 dose. As expected due to the lowest PM_2.5 exposure concentrations, health burden resulting from car travel (with recirculation) caused the least death rates of 0.07 (95% CI 0.07-0.08) prematures deaths per 100,000 commuters/year while microbus with windows-open resulted in the highest death rates; 0.52 (95% CI 0.49-0.56). Microbus deaths represent 57% of national economic losses due to PM_2.5 exposure amongst the four transport modes. This study provides real-time exposure data and analyses its implications on commuter health as a first step in informed decision-making and better urban planning.

Vertical Structure of Air Pollutant Transport Flux as Determined by Ground-Based Remote Sensing Observations in Fen-Wei Plain, China

Air pollutant transport plays an important role in local air quality, but field observations of transport fluxes, especially their vertical distributions, are very limited. We characterized the vertical structures of transport fluxes in central Luoyang, Fen-Wei Plain, China, in winter based on observations of vertical air pollutant and wind profiles using multi-axis differential optical absorption spectroscopy (MAX-DOAS) and Doppler wind lidar, respectively. The northwest and the northeast are the two privileged wind directions. The wind direction and total transport scenarios were dominantly the northwest during clear days, turning to the northeast during the polluted days. Increased transport flux intensities of aerosol were found at altitudes below 400 m on heavily polluted days from the northeast to the southwest over the city. Considering pollution dependence on wind directions and speeds, surface-dominated northeast transport may contribute to local haze events. Northwest winds transporting clean air masses were dominant during clean periods and flux profiles characterized by high altitudes between 200 and 600 m in Luoyang. During the COVID-19 lockdown period in late January and February, clear reductions in transport flux were found for NO2 from the northeast and for HCHO from the northwest, while the corresponding main transport altitude remained unchanged. Our findings provide better understandings of regional transport characteristics, especially at different altitudes.

Three-Year Variations in Criteria Atmospheric Pollutants and Their Relationship with Rainwater Chemistry in Karst Urban Region, Southwest China

Air pollutants have been investigated in many studies, but the variations of atmospheric pollutants and their relationship with rainwater chemistry are not well studied. In the present study, the criteria atmospheric pollutants in nine monitoring stations and rainwater chemistry were analyzed in karst Guiyang city, since the time when the Chinese Ambient Air Quality Standards (CAAQS, third revision) were published. Based on the three-year daily concentration dataset of SO2, NO2, CO, PM10 and PM2.5, although most of air pollutant concentrations were within the limit of CAAQS III-Grade II standard, the significant spatial variations and relatively heavy pollution were found in downtown Guiyang. Temporally, the average concentrations of almost all air pollutants (except for CO) decreased during three years at all stations. Ratios of PM2.5/PM10 in non- and episode days reflected the different contributions of fine and coarse particles on particulate matter in Guiyang, which was influenced by the potential meteorological factors and source variations. According to the individual air quality index (IAQI), the seasonal variations of air quality level were observed, that is, IAQI values of air pollutants were higher in winter (worst air quality) and lower in summer (best air quality) due to seasonal variations in emission sources. The unique IAQI variations were found during the Chinese Spring Festival. Air pollutant concentrations are also influenced by meteorological parameters, in particular, the rainfall amount. The air pollutants are well scoured by the rainfall process and can significantly affect rainwater chemistry, such as SO42−, NO3−, Mg2+, and Ca2+, which further alters the acidification/alkalization trend of rainwater. The equivalent ratios of rainwater SO42−/NO3− and Mg2+/Ca2+ indicated the significant contribution of fixed emission sources (e.g., coal combustion) and carbonate weathering-influenced particulate matter on rainwater chemistry. These findings provide scientific support for air pollution management and rainwater chemistry-related environmental issues.

Study on Coupled Relationship between Urban Air Quality and Land Use in Lanzhou, China

The intensification of global urbanization has exacerbated the negative impact of atmospheric environmental factors in urban areas, thus threatening the sustainability of future urban development. In order to ensure the sustainability of urban atmospheric environments, exploring the changing laws of urban air quality, identifying highly polluted areas in cities, and studying the relationship between air quality and land use have become issues of great concern. Based on AQI data from 340 air quality monitoring stations and urban land use data, this paper uses inverse distance weight (IDW), Getis-Ord Gi*, and a negative binomial regression model to discuss the spatiotemporal variation of air quality in the main urban area of Lanzhou and its relationship with urban land use. The results show that urban air quality has characteristics of temporal and spatial differentiation and spatially has characteristics of agglomeration of cold and hot spots. There is a close relationship between urban land use and air quality. Industrial activities, traffic pollution, and urban construction activities are the most important factors affecting urban air quality. Green spaces can reduce urban pollution. The impact of land use on air quality has a seasonal effect.

Comparison of air pollutants and their health effects in two developed regions in China during the COVID-19 pandemic

Air pollution attributed to substantial anthropogenic emissions and significant secondary formation processes have been reported frequently in China, especially in Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD). In order to investigate the aerosol evolution processes before, in, and after the novel coronavirus (COVID-19) lockdown period of 2020, ambient monitoring data of six air pollutants were analyzed from Jan 1 to Apr 11 in both 2020 and 2019. Our results showed that the six ambient pollutants concentrations were much lower during the COVID-19 lockdown due to a great reduction of anthropogenic emissions. BTH suffered from air pollution more seriously in comparison of YRD, suggesting the differences in the industrial structures of these two regions. The significant difference between the normalized ratios of CO and NO₂ during COVID-19 lockdown, along with the increasing PM_2.5, indicated the oxidation of NO₂ to form nitrate and the dominant contribution of secondary processes on PM_2.5. In addition, the most health risk factor was PM_2.5 and health-risked based air quality index (HAQI) values during the COVID-19 pandemic in YRD in 2020 were all lower than those in 2019. Our findings suggest that the reduction of anthropogenic emissions is essential to mitigate PM_2.5 pollution, while O₃ control may be more complicated.

Examining the status of improved air quality in world cities due to COVID-19 led temporary reduction in anthropogenic emissions

Clean air is a fundamental necessity for human health and well-being. Anthropogenic emissions that are harmful to human health have been reduced substantially under COVID-19 lockdown. Satellite remote sensing for air pollution assessments can be highly effective in public health research because of the possibility of estimating air pollution levels over large scales. In this study, we utilized both satellite and surface measurements to estimate air pollution levels in 20 cities across the world. Google Earth Engine (GEE) and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) application were used for both spatial and time-series assessment of tropospheric Nitrogen Dioxide (NO₂) and Carbon Monoxide (CO) statuses during the study period (1 February to May 11, 2019 and the corresponding period in 2020). We also measured Population-Weighted Average Concentration (PWAC) of particulate matter (PM_2.5 and PM₁₀) and NO₂ using gridded population data and in-situ air pollution estimates. We estimated the economic benefit of reduced anthropogenic emissions using two valuation approaches: (1) the median externality value coefficient approach, applied for satellite data, and (2) the public health burden approach, applied for in-situ data. Satellite data have shown that ~28 tons (sum of 20 cities) of NO₂ and ~184 tons (sum of 20 cities) of CO have been reduced during the study period. PM_2.5, PM₁₀, and NO₂ are reduced by ~37 (μg/m³), 62 (μg/m³), and 145 (μg/m³), respectively. A total of ~1310, ~401, and ~430 premature cause-specific deaths were estimated to be avoided with the reduction of NO₂, PM_2.5, and PM₁₀. The total economic benefits (Billion US$) (sum of 20 cities) of the avoided mortality are measured as ~10, ~3.1, and ~3.3 for NO₂, PM_2.5, and PM₁₀, respectively. In many cases, ground monitored data was found inadequate for detailed spatial assessment. This problem can be better addressed by incorporating satellite data into the evaluation if proper quality assurance is achieved, and the data processing burden can be alleviated or even removed. Both satellite and ground-based estimates suggest the positive effect of the limited human interference on the natural environments. Further research in this direction is needed to explore this synergistic association more explicitly.

Characterization of Products from the Aqueous-Phase Photochemical Oxidation of Benzene-Diols

Chemical processing in atmospheric aqueous phases, including cloud and fog drops, might be significant in reconciling the gap between observed and modeled secondary organic aerosol (SOA) properties. In this work, we conducted a relatively comprehensive investigation of the reaction products generated from the aqueous-phase photochemical oxidation of three benzene-diols (resorcinol, hydroquinone, and methoxyhydroquinone) by hydroxyl radical (·OH), triplet excited state (3C*) 3,4-dimethoxybenzaldehyde (3,4-DMB), and direct photolysis without any added oxidants. The results show that OH-initiated photo-degradation is the fastest of all the reaction systems. For the optical properties, the aqueous oxidation products generated under different reaction conditions all exhibited photo-enhancement upon illumination by simulated sunlight, and the light absorption was wavelength dependent on and increased as a function of the reaction time. The oxygen-to-carbon (O/C) ratio of the products also gradually increased against the irradiation time, indicating the persistent formation of highly oxygenated low-volatility products throughout the aging process. More importantly, aqueous-phase products from photochemical oxidation had an increased oxidative potential (OP) compared with its precursor, indicating they may more adversely impact health. The findings in this work highlight the importance of aqueous-phase photochemical oxidation, with implications for aqueous SOA formation and impacts on both the chemical properties and health effects of OA.

Impact of COVID-related lockdowns on environmental and climate change scenarios

The outbreak of COVID-19 pandemic has emerged as a major challenge from human health perspective. The alarming exponential increase in the transmission and fatality rates related to this disease has brought the world to a halt so as to cope up with its stern consequences. This has led to the imposition of lockdown across the globe to prevent the further spread of this disease. This lock down brought about drastic impacts at social and economic fronts. However, it also posed some positive impacts on environment as well particularly in the context of air quality due to reduction in concentrations of particulate matter (PM), NO₂ and CO across the major cities of the globe as indicated by several research organizations. In China, Italy, France and Spain, there were about 20-30% reduction in NO₂ emission while in USA 30% reduction in NO₂ emission were observed. Compared to previous year, there was 11.4% improvement in the air quality in China. Drastic reductions in NO (-77.3%), NO₂ (-54.3%) and CO (-64.8%) (negative sign indicating a decline) concentrations were observed in Brazil during partial lockdown compared to the five year monthly mean. In India there were about -51.84, -53.11, -17.97, -52.68, -30.35, 0.78 and -12.33% reduction in the concentration of PM_10, PM_2.5, SO_2, NO_2, CO, O₃ and NH₃ respectively. This article highlights the impact of lockdown on the environment and also discusses the pre and post lockdown air pollution scenario across major cities of the world. Several aspect of environment such as air, water, noise pollution and waste management during, pre and post lockdown scenario were studied and evaluated comprehensively. This research would therefore serve as a guide to environmentalist, administrators and frontline warriors for fighting our the way to beat this deadly disease and minimize its long term implications on health and environment.

Impact of the COVID-19 Lockdown on Air Quality and Resulting Public Health Benefits in the Mexico City Metropolitan Area

Meteorology and long-term trends in air pollutant concentrations may obscure the results from short-term policies implemented to improve air quality. This study presents changes in CO, NO2, O3, SO2, PM10, and PM2.5 based on their anomalies during the COVID-19 partial (Phase 2) and total (Phase 3) lockdowns in Mexico City (MCMA). To minimise the impact of the air pollutant long-term trends, pollutant anomalies were calculated using as baseline truncated Fourier series, fitted with data from 2016 to 2019, and then compared with those from the lockdown. Additionally, days with stagnant conditions and heavy rain were excluded to reduce the impact of extreme weather changes. Satellite observations for NO2 and CO were used to contrast the ground-based derived results. During the lockdown Phase 2, only NO2 exhibited significant decreases (p < 0.05) of between 10 and 23% due to reductions in motor vehicle emissions. By contrast, O3 increased (p < 0.05) between 16 and 40% at the same sites where NO2 decreased. During Phase 3, significant decreases (p < 0.05) were observed for NO2 (43%), PM10 (20%), and PM2.5 (32%) in response to the total lockdown. Although O3 concentrations were lower in Phase 3 than during Phase 2, those did not decrease (p < 0.05) from the baseline at any site despite the total lockdown. SO2 decreased only during Phase 3 in a near-road environment. Satellite observations confirmed that NO2 decreased and CO stabilised during the total lockdown. Air pollutant changes during the lockdown could be overestimated between 2 and 10-fold without accounting for the influences of meteorology and long-term trends in pollutant concentrations. Air quality improved significantly during the lockdown driven by reduced NO2 and PM2.5 emissions despite increases in O3, resulting in health benefits for the MCMA population. A health assessment conducted suggested that around 588 deaths related to air pollution exposure were averted during the lockdown. Our results show that to reduce O3 within the MCMA, policies must focus on reducing VOCs emissions from non-mobile sources. The measures implemented during the COVID-19 lockdowns provide valuable information to reduce air pollution through a range of abatement strategies for emissions other than from motor vehicles.

Development of a Health-Based Index to Identify the Association between Air Pollution and Health Effects in Mexico City

Health risks from air pollution continue to be a major concern for residents in Mexico City. These health burdens could be partially alleviated through individual avoidance behavior if accurate information regarding the daily health risks of multiple pollutants became available. A split sample approach was used in this study to create and validate a multi-pollutant, health-based air quality index. Poisson generalized linear models were used to assess the impacts of ambient air pollution (i.e., fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ground-level ozone (O3)) on a total of 610,982 daily emergency department (ED) visits for respiratory disease obtained from 40 facilities in the metropolitan area of Mexico City from 2010 to 2015. Increased risk of respiratory ED visits was observed for interquartile increases in the 4-day average concentrations of PM2.5 (Risk Ratio (RR) 1.03, 95% CI 1.01–1.04), O3 (RR 1.03, 95% CI 1.01–1.05), and to a lesser extent NO2 (RR 1.01, 95% CI 0.99–1.02). An additive, multi-pollutant index was created using coefficients for these three pollutants. Positive associations of index values with daily respiratory ED visits was observed among children (ages 2–17) and adults (ages 18+). The use of previously unavailable daily health records enabled an assessment of short-term ambient air pollution concentrations on respiratory morbidity in Mexico City and the creation of a health-based air quality index, which is now currently in use in Mexico City.

COVID-19 pandemic in Wuhan: Ambient air quality and the relationships between criteria air pollutants and meteorological variables before, during, and after lockdown

As a result of the lockdown (LD) control measures enacted to curtail the COVID-19 pandemic in Wuhan, almost all non-essential human activities were halted beginning on January 23, 2020 when the total lockdown was implemented. In this study, changes in the concentrations of the six criteria air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃) in Wuhan were investigated before (January 1 to 23, 2020), during (January 24 to April 5, 2020), and after the COVID-19 lockdown (April 6 to June 20, 2020) periods. Also, the relationships between the air pollutants and meteorological variables during the three periods were investigated. The results showed that there was significant improvement in air quality during the lockdown. Compared to the pre-lockdown period, the concentrations of NO₂, PM_2.5, PM₁₀, and CO decreased by 50.6, 41.2, 33.1, and 16.6%, respectively, while O₃ increased by 149% during the lockdown. After the lockdown, the concentrations of PM_2.5, CO and SO₂ declined by an additional 19.6, 15.6, and 2.1%, respectively. However, NO₂, O₃, and PM₁₀ increased by 55.5, 25.3, and 5.9%, respectively, compared to the lockdown period. Except for CO and SO₂, WS had negative correlations with the other pollutants during the three periods. RH was inversely related with all pollutants. Positive correlations were observed between temperature and the pollutants during the lockdown. Easterly winds were associated with peak PM_2.5 concentrations prior to the lockdown. The highest PM_2.5 concentrations were associated with southwesterly wind during the lockdown, and northwesterly winds coincided with the peak PM_2.5 concentrations after the lockdown. Although, COVID-19 pandemic had numerous negative effects on human health and the global economy, the reductions in air pollution and significant improvement in ambient air quality likely had substantial short-term health benefits. This study improves the understanding of the mechanisms that lead to air pollution under diverse meteorological conditions and suggest effective ways of reducing air pollution in Wuhan.

Temporal variations and spatial distributions of gaseous and particulate air pollutants and their health risks during 2015-2019 in China

Air quality has been significantly improved in China in recent years; however, our knowledge of the long-term changes in health risks from exposure to air pollutants remain less understood. Here we investigated the temporal variations and spatial distributions of six criteria pollutants (SO₂, NO₂, O₃, CO, PM_2.5 and PM₁₀) in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) during 2015-2019. SO₂ showed 36-60% reductions in three regions, comparatively, NO₂ decreased by 3-17% in BTH and YRD and had a 5% increase in PRD. PM_2.5 and PM₁₀ showed the largest reductions in BTH (30-33%) and the lowest in PRD (7-13%), while O₃ increased by 9% during 2015-2019 particularly in BTH and YRD. Assuming that only air pollutants above given thresholds exert excess risk (ER_total) of mortality, we found that the different variations of pollutants have caused ER_total in BTH decreasing significantly from 4.8% in 2015 to 2.0% in 2019, while from 1.9% to 1.0% in YRD, and a small change in PRD. These results indicate substantially decreased health risks of mortality from exposure to air pollutants as a response to improved air quality. Overall, PM_2.5 dominated ER_total accounting for 42-53% in BTH and 58-64% in YRD with steadily increased contributions, yet ER_total presented strong seasonal dependence on air pollutants with largely increased contribution of O₃ in summer. The ER_total caused by SO₂ was decreased substantially and became negligible except in winter in BTH, while NO₂ only played a role in winter. We also found that ER_PM2.5 was compositional dependent with organics being the major contributor at low ER_PM2.5 while nitrate was more important at high ER_PM2.5. Our results highlight that evaluation of public health risks of air pollution needs to consider chemical differences of PM in different regions in addition to dominant air pollutants in different seasons.

A review of current air quality indexes and improvements under the multi-contaminant air pollution exposure

The air quality is one of the major concerns in the urban environment due to the rapid changes in pollutant emissions driven by complex and intensive human activities. Therefore, quantification of the urban air quality has become an essential need for both urban residents and authorities to quickly assess air quality conditions. To reach this aim, the air quality index (AQI) is the primary way to better understand the urban air quality. However, the varied AQIs in different countries are difficult to directly compare due to the varied calculation methods. Thus, this research presents an updated review of the major AQIs worldwide by dividing them into two categories: single- and multi-contaminant-oriented AQIs. Single-contaminant-oriented AQIs are based on the maximum value of individual pollutants and are applied in most countries with location-dependent standards, such as the United States, China, the United Kingdom and New South Wales, Australia. However, these may greatly underestimate the impact of multiple contaminants, be difficult to dynamically update or to be compared internationally. Moreover, multi-contaminant-oriented AQIs are available in the literature, which consider the combined effects of exposure to multiple contaminants. Among these AQIs, arithmetic pollutant aggregation simply integrates pollutants in a linear or nonlinear way, and weighted pollutant aggregation further assigns varied weights from different perspectives. Combining the advantages and disadvantages of the existing AQIs, the general air quality health index (GAQHI) is proposed as a pollutant-aggregated, local health-based AQI paradigm suitable for the present complex multi-contaminant situation. It provides a direction for the construction of a more accurate, consistent and comparable AQI system and can help both researchers and governments improve human well-being and achieve sustainable development.

Air pollution characteristics, health risks, and source analysis in Shanxi Province, China

China is confronting an unprecedented air pollution problem. This study discussed the characteristics of air pollution and its risks on human health and conducted source analysis combined with local development in Shanxi Province in 2016 and 2017. Results demonstrated that the air pollution situation in Shanxi was deteriorating, with Taiyuan, Yangquan, Changzhi, Jincheng, Jinzhong, and Linfen being heavily polluted districts. Particulate matter (PM) was considered the major pollutant, but nitrogen dioxide and ozone showed a dominant trend recently. Furthermore, the health risks evaluated on the basis of a comprehensive air quality index (AAQI) and an aggregated risk index revealed a relatively high-risk level in Shanxi. Among the pollutants, the largest contributor was PM, followed by sulfur dioxide and ozone. Southern Shanxi had the largest pollution level and health risks, whereas Datong was the least polluted region. Source analysis suggested that the main driving forces of air pollution, besides natural factors, were urbanization, population size, civil vehicles, coal-based heavy industries, and high-energy consumption. Therefore, strengthening urban greening, vigorously adjusting and optimizing the industrial structure, and formulating a multi-domain cooperative control regime on air pollution, especially PM and ozone, should be promoted.

Impacts of the COVID-19 event on the NOx emissions of key polluting enterprises in China

The unprecedented cessation of human activities during the COVID-19 pandemic has affected China's industrial production and NOx emissions. Quantifying the changes in NOx emissions resulting from COVID-19 and associated governmental control measures is crucial to understanding its impacts on the environment. Here, we divided the research timeframe into three periods: the normal operation period (P1), the Spring Festival period (P2), and the epidemic period following the Spring Festival (P3). We then calculated the NOx operating vent numbers and emission concentrations of key polluting enterprises in 29 provinces and 20 industrial sectors and compared the data for the same periods in 2020 and 2019 to obtain the impacts of COVID-19 on industrial NOx emissions. We found that spatially, from P1 to P2 in 2020, the operating NOx vent numbers in North China changed the most, with a relative change rate of -33.84%. Comparing the operating vent numbers in P1 and P3, East China experienced the largest decrease, approximately -32.72%. Among all industrial sectors, the mining industry, manufacturing industry, power, heat, gas, and water production and supply industry, and the wholesale and retail industry, were the most heavily influenced. In general, the operating vent numbers of key polluting enterprises in China decreased by 24.68%, and the standardized NOx (w)_5-day decreased by an average of -9.54 ± -6.00 due to the COVID-19 pandemic. The results suggest that COVID-19 significantly reduced the NOx emission levels of the key polluting enterprises in China.

Air Pollution Characteristics and Health Risks in the Yangtze River Economic Belt, China during Winter

The air pollution characteristics of six ambient criteria pollutants, including particulate matter (PM) and trace gases, in 29 typical cities across the Yangtze River Economic Belt (YREB) from December 2017 to February 2018 are analyzed. The overall average mass concentrations of PM_2.5, PM₁₀, SO₂, CO, NO₂, and O₃ are 73, 104, 16, 1100, 47, and 62 µg/m³, respectively. PM_2.5, PM₁₀, and NO₂ are the dominant major pollutants to poor air quality, with nearly 83%, 86%, and 59%, exceeding the Chinese Ambient Air Quality Standard Grade I. The situation of PM pollution in the middle and lower reaches is more serious than that in the upper reaches, and the north bank is more severe than the south bank of the Yangtze River. Strong positive spatial correlations for PM concentrations between city pairs within 300 km is frequently observed. NO₂ pollution is primarily concentrated in the Suzhou-Wuxi-Changzhou urban agglomeration and surrounding areas. The health risks are assessed by the comparison of the classification of air pollution levels with three approaches: air quality index (AQI), aggregate AQI (AAQI), and health risk-based AQI (HAQI). When the AQI values escalate, the air pollution classifications based on the AAQI and HAQI values become more serious. The HAQI approach can better report the comprehensive health effects from multipollutant air pollution. The population-weighted HAQI data in the winter exhibit that 50%, 70%, and 80% of the population in the upstream, midstream, and downstream of the YREB are exposed to polluted air (HAQI > 100). The current air pollution status in YREB needs more effective efforts to improve the air quality.

Effects of China's current Air Pollution Prevention and Control Action Plan on air pollution patterns, health risks and mortalities in Beijing 2014-2018

Beijing is one of the most polluted cities in the world. However, the "Air Pollution Prevention and Control Action Plan" (APPCAP), introduced since 2013 in China, has created an unprecedented drop in pollution concentrations for five major pollutants, except O₃, with a significant drop in mortalities across most parts of the city. To assess the effects of APPCAP, air pollution data were collected from 35 sites (divided into four types, namely, urban, suburban, regional background, and traffic) in Beijing, from 2014 to 2018 and analyzed. Simultaneously, health-risk based air quality index (HAQI) and district-specific pollution (PM_2.5 and O₃) attributed mortality were calculated for Beijing. The results show that the annual PM_2.5 concentration exceeded the Chinese national ambient air quality standard Grade II (35 μg/m³) in all sites, ranging from 88.5 ± 77.4 μg/m³ for the suburban site to 98.6 ± 89.0 μg/m³ for the traffic site in 2014, but was reduced to 50.6 ± 46.6 μg/m³ for the suburban site, and 56.1 ± 47.0 μg/m³ for the regional background in 2018. O₃ was another most important pollutant that exceeded the Grade II standard (160 μg/m³) for a total of 291 days. It peaked at 311.6 μg/m³ in 2014 for the urban site and 290.6 μg/m³ in 2018 in the suburban site. APPCAP led to a significant reduction in PM_2.5, PM₁₀, NO₂, SO₂ and CO concentrations by 7.4, 8.1, 2.4, 1.9 and 80 μg/m³/year respectively, though O₃ concentration was increased by 1.3 μg/m³/year during the five-years. HAQI results suggest that during the high pollution days, the more vulnerable groups, such as the children, and the elderly, should take additional precautions, beyond the recommendations currently put forward by Beijing Municipal Environmental Monitoring Center (BJMEMC). In 2014, PM_2.5 and O₃ attributed to 29,270 and 3,030 deaths respectively, though in 2018 their mortalities were reduced by 5.6% and 18.5% respectively. The highest mortality was observed in Haidian and Chaoyang districts, two of the most densely populated areas in Beijing. Beijing's air quality has seen a dramatic improvement over the five-year period, which can be attributable to the implementation of APPCAP and the central government's determination, with significant drops in the mortalities due to PM_2.5 and O₃ in parallel. To further improve air quality in Beijing, more stringent regulatory measures should be introduced to control volatile organic compounds (VOCs) and reduce O₃ concentrations. Consistent air pollution control interventions will be needed to ensure long-term prosperity and environmental sustainability in Beijing, China's most powerful city. This study provides a robust methodology for analyzing air pollution trends, health risks and mortalities in China. The crucial evidence generated forms the basis for the governments in China to introduce location-specific air pollution policy interventions to further reduce air pollution in Beijing and other parts of China. The methodology presented in this study can form the basis for future fine-grained air pollution and health risk study at the city-district level in China.

Temporary reduction in fine particulate matter due to 'anthropogenic emissions switch-off' during COVID-19 lockdown in Indian cities

The COVID-19 pandemic elicited a global response to limit associated mortality, with social distancing and lockdowns being imposed. In India, human activities were restricted from late March 2020. This 'anthropogenic emissions switch-off' presented an opportunity to investigate impacts of COVID-19 mitigation measures on ambient air quality in five Indian cities (Chennai, Delhi, Hyderabad, Kolkata, and Mumbai), using in-situ measurements from 2015 to 2020. For each year, we isolated, analysed and compared fine particulate matter (PM2.5) concentration data from 25 March to 11 May, to elucidate the effects of the lockdown. Like other global cities, we observed substantial reductions in PM2.5 concentrations, from 19 to 43% (Chennai), 41-53% (Delhi), 26-54% (Hyderabad), 24-36% (Kolkata), and 10-39% (Mumbai). Generally, cities with larger traffic volumes showed greater reductions. Aerosol loading decreased by 29% (Chennai), 11% (Delhi), 4% (Kolkata), and 1% (Mumbai) against 2019 data. Health and related economic impact assessments indicated 630 prevented premature deaths during lockdown across all five cities, valued at 0.69 billion USD. Improvements in air quality may be considered a temporary lockdown benefit as revitalising the economy could reverse this trend. Regulatory bodies must closely monitor air quality levels, which currently offer a baseline for future mitigation plans.

Air pollution characteristics and human health risks in key cities of northwest China

Air pollution events occur frequently in northwest China, which results in serious detrimental effects on human health. Therefore, it is essential to understand the air pollution characteristics and assess the risks to humans. In this study, we analyzed the pollution characteristics of criteria pollutants in six key cities in northwest China from 2015 to 2018. We used the air quality index (AQI), aggregate AQI (AAQI), and health-risk based AQI (HAQI) to assess the health risks and determine the proportion of people exposed to air pollution. Additionally, on this basis, the AirQ_2.2.3 model was used to quantify the health effects of the pollutants. The results showed that PM₁₀ pollution occurred mainly in spring and winter and was caused by frequent dust storms. PM_2.5 pollution was caused mainly by anthropogenic activities (especially coal-fired heating in winter). Because of a series of government policies and pollutant reduction measures, PM_2.5, SO₂, NO₂, and CO concentrations showed a downward trend during the study period (except for a small increase in the case of NO₂ in some years.). However, O₃ showed high concentrations due to the high intensity of solar radiation in summer and inadequate emission reduction measures. The air quality levels based on their classification were generally higher than the Chinese ambient air quality standard classified by the AQI index. We also found that the higher the AQI index was, the more serious the air pollution classified based on the AAQI and HAQI indices was. The HAQI index could better reflect the impact of pollutants on human health. Based on the HAQI index, 20% of the population in the study area was exposed to polluted air. The total mortality values attributable to PM₁₀, PM_2.5, SO₂, O₃, NO₂, and CO, quantified by the AirQ_2.2.3 model, were 3.00%, 1.02%, 1.00%, 4.22%, 1.57%, and 0.95% (Confidence Interval:95%), respectively; the attributable proportions of mortality for respiratory system and cardiovascular diseases were consistent with the change rule of total mortality, because the number of deaths attributable to the latter was greater than that for the former. According to the exposure reaction curves of pollutants, PM₁₀ and PM_2.5 still showed a large change at high concentrations. However, the tendencies of SO₂, NO₂, CO, and O₃ were more obvious under low concentration exposure, which indicated that the expected mortality rate due to lower air pollution concentrations was much higher than the mortality due to high air pollution concentrations.

Effect of restricted emissions during COVID-19 on air quality in India

The effectiveness and cost are always top factors for policy-makers to decide control measures and most measures had no pre-test before implementation. Due to the COVID-19 pandemic, human activities are largely restricted in many regions in India since mid-March of 2020, and it is a progressing experiment to testify effectiveness of restricted emissions. In this study, concentrations of six criteria pollutants, PM₁₀, PM_2.5, CO, NO₂, ozone and SO₂ during March 16th to April 14th from 2017 to 2020 in 22 cities covering different regions of India were analysed. Overall, around 43, 31, 10, and 18% decreases in PM_2.5, PM₁₀, CO, and NO₂ in India were observed during lockdown period compared to previous years. While, there were 17% increase in O₃ and negligible changes in SO₂. The air quality index (AQI) reduced by 44, 33, 29, 15 and 32% in north, south, east, central and western India, respectively. Correlation between cities especially in northern and eastern regions improved in 2020 compared to previous years, indicating more significant regional transport than previous years. The mean excessive risks of PM reduced by ~52% nationwide due to restricted activities in lockdown period. To eliminate the effects of possible favourable meteorology, the WRF-AERMOD model system was also applied in Delhi-NCR with actual meteorology during the lockdown period and an un-favourable event in early November of 2019 and results show that predicted PM_2.5 could increase by only 33% in unfavourable meteorology. This study gives confidence to the regulatory bodies that even during unfavourable meteorology, a significant improvement in air quality could be expected if strict execution of air quality control plans is implemented.

Investigation of the impacts of urban vegetation loss on the ecosystem service of air pollution mitigation in Karaj metropolis, Iran

The present study aims to investigate the relationship between reduced air pollution and ecosystem services in Karaj metropolis, Iran. To the end, the trends in the concentrations of O₃, NO₂, CO, SO₂, PM₁₀, and PM_2.5 as the main atmospheric pollutants of Karaj were studied. Five time series models of autoregressive (AR), moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA) were used to predict changes in air pollutant concentrations. Air pollution zoning is conducted via ArcGIS_10.3 by using spline tension interpolation method. Then, normalized difference vegetation index (NDVI) was obtained from Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images to analyze vegetation dynamics as an index of ecosystem functioning. NDVI thresholds were selected to present guidelines for qualitative and quantitative changes in green cover and were divided into five different categories. Based on the results, AR (1) and ARIMA (1,2,1) were recognized as appropriate models for predicting the concentration of air pollutants in the study area. A decrease in very dense vegetation coverage and increase in poor vegetation areas, followed by an increase in air pollution, revealed that the loss of urban green coverage and decreased ecosystem services were positively related. Furthermore, the expansion of urban lands toward the north and the west from the baseline to future condition led to great changes in the land cover and losses in vegetation along these axes, which finally resulted in increased air pollution in these areas. Thus, the results of this study can be directly used in decision-making in the area of air pollution.

Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak

Due to the pandemic of coronavirus disease 2019 in China, almost all avoidable activities in China are prohibited since Wuhan announced lockdown on January 23, 2020. With reduced activities, severe air pollution events still occurred in the North China Plain, causing discussions regarding why severe air pollution was not avoided. The Community Multi-scale Air Quality model was applied during January 01 to February 12, 2020 to study PM_2.5 changes under emission reduction scenarios. The estimated emission reduction case (Case 3) better reproduced PM_2.5. Compared with the case without emission change (Case 1), Case 3 predicted that PM_2.5 concentrations decreased by up to 20% with absolute decreases of 5.35, 6.37, 9.23, 10.25, 10.30, 12.14, 12.75, 14.41, 18.00 and 30.79 μg/m³ in Guangzhou, Shanghai, Beijing, Shijiazhuang, Tianjin, Jinan, Taiyuan, Xi'an, Zhengzhou, Wuhan, respectively. In high-pollution days with PM_2.5 greater than 75 μg/m³, the reductions of PM_2.5 in Case 3 were 7.78, 9.51, 11.38, 13.42, 13.64, 14.15, 14.42, 16.95 and 22.08 μg/m³ in Shanghai, Jinan, Shijiazhuang, Beijing, Taiyuan, Xi'an, Tianjin, Zhengzhou and Wuhan, respectively. The reductions in emissions of PM_2.5 precursors were ~2 times of that in concentrations, indicating that meteorology was unfavorable during simulation episode. A further analysis shows that benefits of emission reductions were overwhelmed by adverse meteorology and severe air pollution events were not avoided. This study highlights that large emissions reduction in transportation and slight reduction in industrial would not help avoid severe air pollution in China, especially when meteorology is unfavorable. More efforts should be made to completely avoid severe air pollution.

Improved risk communications with a Bayesian multipollutant Air Quality Health Index

Establishing an optimal indicator to communicate health risks of multiple air pollutants to public is much important. The Air Quality Health Index (AQHI) has been developed in many countries as a communication tool of multiple air pollutants related health risks. However, the current AQHI is based on the sum of the excess health risks which are typically derived from the single-pollutant statistical models. Such a strategy may overestimate the joint effect of multiple pollutants. We proposed an improved strategy to construct the AQHI based on a Bayesian multipollutant weighted model. Using this strategy, two improved indices - Bayesian multipollutant AQHI (BMP-AQHI) and Bayesian multipollutant AQHI with seasonal specificity (SBMP-AQHI) were calculated to present the multiple pollutants related health risks to the cardiovascular system based on data collected in Chengdu, China during 2013 to 2018. The two improved indices were compared to current Air Quality Index (AQI) and AQHI to evaluate the effectiveness of the improved indices in characterizing multipollutant health risks. The AQI risk classification suggested much smaller health risks than AQHIs. Among three AQHI types, the BMP-AQHI and SBMP-AQHI suggested slightly lower health risks to the cardiovascular system than the current AQHI. In the evaluation analysis, the SBMP-AQHI had the strongest association with the mortality of cardiovascular disease (CVD) (2.66%; 95%CI, 1.57%, 3.76%). In the subgroup analysis, an interquartile increase (IQR) of the SBMP-AQHI was associated with 3.21% (95%CI, 2.06%, 4.38%), 1.34% (95%CI, -0.13%, 2.82%), and 4.20% (95%CI, 2.59%, 5.84%) increases for CVD mortality in the elderly, male, and female subgroups, respectively. The study shows that the improved AQHIs can communicate the health information of multiple air pollutants more efficiently. The study also indicates the necessity to consider seasonal specificity in the construction of the AQHI.

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.