Seasonal changes in the recent decline of combined high PM2.5 and O3 pollution and associated chemical and meteorological drivers in the Beijing-Tianjin-Hebei region, China

Distinct responses of urban and rural O3 pollution with secondary particle changes to anthropogenic emission reductions: Insights from a case study over North China

Ozone (O3) pollution with excessive near-surface O3 levels has been an important environmental issue in China, although the anthropogenic emission reductions (AER) have improved air quality since 2013. In this study, we investigated the sensitivities of atmospheric chemical environment with the urban and rural changes to the AER targeting a typical O3 pollution episode over North China in summer 2019, by conducting two WRF-Chem simulation experiments under two scenarios of anthropogenic emission inventories of years 2012 and 2019 with the meteorological conditions in the 2019 summertime O3 pollution episode for excluding the meteorological impacts on O3 pollution. The results show that the unbalanced AER aroused more serious O3 pollution in urban and rural areas. The intense NO reduction was responsible for the significant increments of urban O3, while the falling NO2 and NO synergistically devoted to the slight O3 variations in rural areas. Induced by the recent-year AER, the urban O3 production was governed by VOCs-limited and transition regime, whereas the NOx-limited regime dominated over rural areas in North China. Also, the AER reinforced the atmospheric oxidation capacity with the elevations of atmospheric oxidants O3 and ROx radicals, strengthening the chemical conversions to secondary inorganic particles. In both urban and rural areas, the sharp drop in SO2 caused a decrease in sulfate fraction, while the enhanced AOC accelerated the transformation to nitrate even when NOx was reduced. The AER induced nitrate to occupy the principal position in secondary PM2.5 in urban and rural areas. The AER promoted daytime and suppressed nighttime the nitrate production in urban areas, and more vigorous conversion of secondary aerosols were found in rural areas with much lower AOC increments. This study provides insights from a case study over North China in distinct responses of urban and rural O3 pollution with secondary particle changes to AER in urban and rural atmospheric environment changes, with implications for an effective abatement strategy on O3 pollution.

Multi-scale analysis of the chemical and physical pollution evolution process from pre-co-pollution day to PM2.5 and O3 co-pollution day

PM2.5 and O3 are two of the main air pollutants that have adverse impacts on climate and human health. The evolution process of PM2.5 and O3 co-pollution are of concern because of the increased frequency of PM2.5 and O3 co-pollution days. Here, we examined the chemical coupling and revealed the driving factors of the PM2.5 and O3 co-pollution evolution process from cleaning day, PM2.5 pollution day, or O3 pollution day, applied by theoretical analysis and model calculation methods. The results demonstrate that PM2.5 and O3 co-pollution day frequently occurred with high concentrations of gaseous precursors and higher sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR), which we attribute to the enhancement of atmospheric oxidation capacity (AOC). The AOC is positively correlated with O3 and weakly correlated with PM2.5. In addition, we found that the correlation coefficients of PM2.5-NO2 (0.62) were higher than that of PM2.5-SO2 (0.32), highlighting the priority of NOx controlling to mitigate PM2.5 pollution. Overall, our discovery can provide scientific evidence to design feasible solutions for the controlling PM2.5 and O3 co-pollution process.

A novel hybrid features grey incidence model and its application in identifying key factors influencing air pollution in Jiangsu Province

Accurately assessing the key factors influencing air pollution is crucial for effective air pollution control. To address this need, we propose a novel Hybrid Features Grey Incidence Model (HFGIM), which integrates geometric feature differences from both proximity and similarity perspectives. Firstly, we extract geometric feature difference vectors of proximity and similarity from time series data and measure the overall feature difference degree by calculating vector norms. Secondly, we calculate the relative feature differences and information contribution rates of proximity and similarity to derive the hybrid feature differences coefficient between sequences, thereby obtaining the hybrid features incidence degree. After detailing the model's properties and modeling steps, we introduce the Cross-sectional Data Hybrid Features Grey Incidence Model (C-HFGIM) and the Panel Data Hybrid Features Grey Incidence Model (P-HFGIM) for handling cross-sectional and panel data, respectively. Applying HFGIM, we identified the key pollutants and primary pollution source indicators of air pollution in Jiangsu Province. We also compared HFGIM with other classical grey incidence models to verify the proposed model's effectiveness. Based on the analysis results, we propose policy recommendations for air pollution control in Jiangsu Province.

Co-occurrence of ozone and PM2.5 pollution in urban/non-urban areas in eastern China from 2013 to 2020: Roles of meteorology and anthropogenic emissions

We applied a three-dimensional (3-D) global chemical transport model (GEOS-Chem) to evaluate the influences of meteorology and anthropogenic emissions on the co-occurrence of ozone (O3) and fine particulate matter (PM2.5) pollution day (O3-PM2.5PD) in urban and non-urban areas of the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions during the warm season (April-October) from 2013 to 2020. The model captured the observed O3-PM2.5PD trends and spatial distributions well. From 2013 to 2020, with changes in both anthropogenic emissions and meteorology, the simulated values of O3-PM2.5PD in the urban (non-urban) areas of the BTH and YRD regions were 424.8 (330.1) and 309.3 (286.9) days, respectively, suggesting that pollution in non-urban areas also warrants attention. The trends in the simulated values of O3-PM2.5PD were -0.14 and -0.15 (+1.18 and +0.81) days yr-1 in the BTH (YRD) urban and non-urban areas, respectively. Sensitivity simulations revealed that changes in anthropogenic emissions decreased the occurrence of O3-PM2.5PD, with trends of -0.99 and -1.23 (-1.47 and -1.92) days yr-1 in the BTH (YRD) urban and non-urban areas, respectively. Conversely, meteorological conditions could exacerbate the frequency of O3-PM2.5PD, especially in the urban YRD areas, but less notably in the urban BTH areas, with trends of +2.11 and +0.30 days yr-1, respectively, owing to changes in meteorology only. The increases in T2m_max and T2m were the main meteorological factors affecting O3-PM2.5PD in most BTH and YRD areas. Furthermore, by conducting sensitivity experiments with different levels of pollutant precursor reductions in 2020, we found that volatile organic compound (VOC) reductions primarily benefited O3-PM2.5PD decreases in urban areas and that NOx reductions more notably influenced those in non-urban areas, especially in the YRD region. Simultaneously, reducing VOC and NOx emissions by 50 % resulted in considerable O3-PM2.5PD decreases (58.8-72.6 %) in the urban and non-urban areas of the BTH and YRD regions. The results of this study have important implications for the control of O3-PM2.5PD in the urban and non-urban areas of the BTH and YRD regions.

Quantifying the scale-dependent relationships of PM2.5 and O3 on meteorological factors and their influencing factors in the Beijing-Tianjin-Hebei region and surrounding areas

To investigate the variations of PM2.5 and O3 and their synergistic effects with influencing factors at different time scales, we employed Bayesian estimator of abrupt seasonal and trend change to analyze the nonlinear variation process of PM2.5 and O3. Wavelet coherence and multiple wavelet coherence were utilized to quantify the coupling oscillation relationships of PM2.5 and O3 on single/multiple meteorological factors in the time-frequency domain. Furthermore, we combined this analysis with the partial wavelet coherence to quantitatively evaluate the influence of atmospheric teleconnection factors on the response relationships. The results obtained from this comprehensive analysis are as follows: (1) The seasonal component of PM2.5 exhibited a change point, which was most likely to occur in January 2017. The trend component showed a discontinuous decline and had a change point, which was most likely to appear in February 2017. The seasonal component of O3 did not exhibit a change point, while the trend component showed a discontinuous rise with two change points, which were most likely to occur in July 2018 and May 2017. (2) The phase and coherence relationships of PM2.5 and O3 on meteorological factors varied across different time scales. Stable phase relationships were observed on both small- and large-time scales, whereas no stable phase relationship was formed on medium scales. On all-time scales, sunshine duration was the best single variable for explaining PM2.5 variations and precipitation was the best single variable explaining O3 variations. When compared to single meteorological factors, the combination of multiple meteorological factors significantly improved the ability to explain variations in PM2.5 and O3 on small-time scales. (3) Atmospheric teleconnection factors were important driving factors affecting the response relationships of PM2.5 and O3 on meteorological factors and they had greater impact on the relationship at medium-time scales compared to small- and large-time scales.

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.

The rationale behind updates to ambient ozone guidelines and standards

Although air quality has gradually improved in recent years, as shown by the decrease in PM2.5 concentration, the problem of rising ambient ozone has become increasingly serious. To reduce hazards to human health and environmental welfare exposure to ozone, scientists and government regulators have developed ozone guidelines and standards. These answer the questions of which levels of exposure are hazardous to human health and the environment, and how can ambient ozone exposure be guaranteed, respectively. So what are the basis for the ozone guidelines and standards? This paper reviews in detail the process of revising ozone guidelines and standards by the World Health Organization (WHO) and the United States Environmental Protection Agency (EPA). The present study attempts to explore and analyze the scientific basis and empirical methods for updating guidelines and standards, in a view to guide the future revision process and provide directions for further scientific research. We found many epidemiological and toxicological studies and exposure-response relationships provided strong support for developing and revising the ozone guidelines. When setting standards, ozone exposure has been effectively considered, and the economic costs, health, and indirect economic benefits of standard compliance were reasonably estimated. Accordingly, epidemiological and toxicological studies and the establishment of exposure-response relationships, as well as exposure and risk assessment and benefit-cost estimates of standards compliance should be strengthened for the further update of guidelines and standards. In addition, with the increasing prominence of combined air pollution led by ozone and PM2.5, more joint exposure scientific research related to ozone guidelines and standards should be undertaken.

Short-term joint effects of ambient PM2.5 and O3 on mortality in Beijing, China

Introduction

In recent years, air pollution caused by co-occurring PM2.5 and O3, named combined air pollution (CAP), has been observed in Beijing, China, although the health effects of CAP on population mortality are unclear.

Methods

We employed Poisson generalized additive models (GAMs) to evaluate the individual and joint effects of PM2.5 and O3 on mortality (nonaccidental, respiratory, and cardiovascular mortality) in Beijing, China, during the whole period (2014-2016) and the CAP period. Adverse health effects were assessed for percentage increases (%) in the three mortality categories with each 10-μg/m3 increase in PM2.5 and O3. The cumulative risk index (CRI) was adopted as a novel approach to quantify the joint effects.

Results

The results suggested that both PM2.5 and O3 exhibited the greatest individual effects on the three mortality categories with cumulative lag day 01. Increases in the nonaccidental, cardiovascular, and respiratory mortality categories were 0.32%, 0.36%, and 0.43% for PM2.5 (lag day 01) and 0.22%, 0.37%, and 0.25% for O3 (lag day 01), respectively. There were remarkably synergistic interactions between PM2.5 and O3 on the three mortality categories. The study showed that the combined effects of PM2.5 and O3 on nonaccidental, cardiovascular, and respiratory mortality were 0.34%, 0.43%, and 0.46%, respectively, during the whole period and 0.58%, 0.79%, and 0.75%, respectively, during the CAP period. Our findings suggest that combined exposure to PM2.5 and O3, particularly during CAP periods, could further exacerbate their single-pollutant health risks.

Conclusion

These findings provide essential scientific evidence for the possible creation and implementation of environmental protection strategies by policymakers.

Air pollution in heavy industrial cities along the northern slope of the Tianshan Mountains, Xinjiang: characteristics, meteorological influence, and sources

The spatiotemporal characteristics, relationship with meteorological factors, and source distribution of air pollutants (January 2017-December 2021) were analyzed to better understand the air pollutants on the northern slope of the Tianshan Mountains (NSTM) in Xinjiang, a heavily polluted urban agglomeration of heavy industries. The results showed that the annual mean concentrations of SO₂, NO₂, CO, O₃, PM_2.5, and PM₁₀ were 8.61-13.76 μg m^-3, 26.53-36.06 μg m^-3, 0.79-1.31 mg m^-3, 82.24-87.62 μg m^-3, 37.98-51.10 μg m^-3, and 84.15-97.47 μg m^-3. The concentrations of air pollutants (except O₃) showed a decreasing trend. The highest concentrations were in winter, and in Wujiaqu, Shihezi, Changji, Urumqi, and Turpan, the concentrations of particulate matter exceeded the NAAQS Grade II during winter. The west wind and the spread of local pollutants both substantially impacted the high concentrations. According to the analysis of the backward trajectory in winter, the air masses were mainly from eastern Kazakhstan and local emission sources, and PM₁₀ in the airflow had a more significant impact on Turpan; the rest of the cities were more affected by PM_2.5. Potential sources included Urumqi-Changj-Shihezi, Turpan, the northern Bayingol Mongolian Autonomous Prefecture, and eastern Kazakhstan. Consequently, the emphasis on improving air quality should be on reducing local emissions, strengthening regional cooperation, and researching transboundary transport of air pollutants.

Spatiotemporal Distribution Patterns and Exposure Risks of PM2.5 Pollution in China

The serious pollution of PM2.5 caused by rapid urbanization in recent years has become an urgent problem to be solved in China. Annual and daily satellite-derived PM2.5 datasets from 2001 to 2020 were used to analyze the temporal and spatial patterns of PM2.5 in China. The regional and population exposure risks of the nation and of urban agglomerations were evaluated by exceedance frequency and population weight. The results indicated that the PM2.5 concentrations of urban agglomerations decreased sharply from 2014 to 2020. The region with PM2.5 concentrations less than 35 μg·m−3 accounted for 80.27% in China, and the average PM2.5 concentrations in 8 urban agglomerations were less than 35 μg·m−3 in 2020. The spatial distribution pattern of PM2.5 concentrations in China revealed higher concentrations to the east of the Hu Line and lower concentrations to the west. The annual regional exposure risk (RER) in China was at a high level, with a national average of 0.75, while the average of 14 urban agglomerations was as high as 0.86. Among the 14 urban agglomerations, the average annual RER was the highest in the Shandong Peninsula (0.99) and lowest in the Northern Tianshan Mountains (0.76). The RER in China has obvious seasonality; the most serious was in winter, and the least serious was in summer. The population exposure risk (PER) east of the Hu Line was significantly higher than that west of the Hu Line. The average PER was the highest in Beijing-Tianjin-Hebei (4.09) and lowest in the Northern Tianshan Mountains (0.71). The analysis of air pollution patterns and exposure risks in China and urban agglomerations in this study could provide scientific guidance for cities seeking to alleviate air pollution and prevent residents’ exposure risks.