Pollution In China Research Articles

Analysis of PM2.5 Variations Based on Observed, Satellite-Derived, and Population-Weighted Concentrations

Fine particulate matter (PM2.5), which can cause adverse human health effects, has been proven as the first air pollutant in China. In situ observations with ground-level monitoring and satellite-based concentrations have been used to analyze the variations in PM2.5. However, variation analyses based on these two kinds of measurement have mainly focused on the concentration itself and ignored the effects on the population. Therefore, this study not only investigated these two kinds of measurements, but also performed weighted population analyses to study the variations in PM2.5. Firstly, daily models of timely structure adaptive modeling (TSAM) were constructed to simulate satellite-derived PM2.5 levels from January 2013 to December 2016. Secondly, population-weighted concentrations were calculated based on TSAM-derived PM2.5 surfaces. Finally, observed, TSAM-derived, and population-weighted concentrations were used to analyze the variations in PM2.5. The results showed the different importance of various input parameters; AOD had the highest rank. Additionally, TSAM models demonstrated good performance, fitting R ranging from 0.86 to 0.91, and validating R from 0.82 to 0.89. According to the air quality standard in China, TSAM-derived PM2.5 showed that the increase in area lower than Level II was 29.03% and the increase in population was only 14.81%. This indicates that the air quality exhibited an overall improvement in spatial perspective, but some areas with high population density showed a relatively low improvement due to uneven distributions in China. The population-weighted PM2.5 concentration could better represent the health threats of air pollutants compared with in situ observations.

Drivers of PM2.5 in the urban agglomeration on the northern slope of the Tianshan Mountains, China

Fine particulate matter (PM2.5) is a major source of air pollution in China. Although there have been many studies of the drivers of PM2.5 pollution in the megacities clustered in eastern China, the behavior of PM2.5 in the northwestern urban agglomeration is not well understood. This study used near-surface observation data for 2015–2019 obtained from the national air environmental monitoring network to examine variation in PM2.5 in the urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM). Two-factor interaction provided new insights into the dominant factors of PM2.5 in the study region. The annual average PM2.5 concentrations over the study period was 54.3 μg/m3, with an exceedance rate of 23.3%. Wavelet analysis showed two dominant cycles of 320–370 d and 150–200 d with high pollution events occurring in winter. The generalized additive model (GAM) contained linear functions of pressure, non-linear functions of SO2, NO2, relative humidity, sunshine duration and temperature. The two most primary variables, NO2 and SO2, represent 20.65% and 19.54% of the total deviance explained, respectively, while the meteorological factors account for 36.1% of the total deviance explained. In addition, the interaction between NO2 and other factors had the strongest effect on PM2.5. The deviance explained in the two factor interaction model (88.5%) was higher than that in the single factor model (78.4%). Our study emphasized that interaction between meteorological factors and pollutant emissions enhanced the impact on PM2.5 compared with individual factors, which can provide a scientific basis for developing effective emission reduction strategies in UANSTM.

Fiscal Decentralization, Green Technology Innovation, and Regional Air Pollution in China: An Investigation from the Perspective of Intergovernmental Competition.

Fiscal decentralization (FD), as an institutional arrangement for the fiscal division between central and local governments, gives local governments the enthusiasm and autonomy to provide public products and services. With the dominance of environmental governance, how local governments can avoid intergovernmental “race to the bottom” issues through green technology innovation (GTI) is a matter of regional green development and continuous improvement of atmospheric environmental quality. Based on a sample of 30 provinces in China from 2003 to 2018, this paper uses the spatial Durbin model (SDM) to examine the relationship between FD, GTI, and regional air pollution and explores their spatial spillover effect and regional heterogeneity from the perspective of intergovernmental competition. The results indicate that the FD and GTI in various provinces had significant and regionally differentiated inhibitory effects on local air pollution. In Western China, due to the regional competition among local governments in terms of economic development, economic development-oriented fiscal expenditures crowd out environmental governance-oriented fiscal expenditures, which has led to the consequence that FD can intensify local air pollution and has a positive spillover effect, but the demonstration effect of green technological innovation can well moderate the effect of FD on air pollution. FD in the eastern region has played a positive role in promoting regional air quality improvement. However, its green technological innovation has not played a positive role in reducing emissions, and it plays a significant negative regulatory role in the emission reduction effect led by FD. Finally, the article puts forward policy recommendations in terms of a fiscal decentralization system, green technological innovation, and performance evaluation mechanism.

The spatial impact of atmospheric environmental policy on public health based on the mediation effect of air pollution in China.

The topic of air pollution and its effect on public health has become a hot policy issue that has attracted worldwide attention, but this attention has seldom been extended to the causal relationship between atmospheric environmental policy (AEP), air pollution, and public health. This paper uses panel data from 30 provinces in China to construct spatial econometric models that analyze the impact of AEP on air pollution, the impact of air pollution on public health, and the mediation effect that air pollution may have between AEP and public health. The results demonstrate that there is a significant positive spatial spillover effect of soot and dust (SD) emission intensity and the overall air pollution level as measured by the Air Pollution Index (API). The AEP has significant inhibitory effects on the intensity of sulfur dioxide and SD emissions, as well as on overall air pollution. An increase in the overall air pollution level has a significant detrimental effect on public health as measured by average life expectancy. Air pollution as measured by API is a mediating factor in the relationship between AEP and public health. The study results could help to effectively control air pollution and promote public health by leading to improvements in regional pollution prevention and control mechanisms and strengthening of the central government's policy formulation and local governments' policy implementation process.

Turnover of Local Government Officials and Local Air Quality

AbstractThis paper constructs a fixed effect model to study the impact of turnover of local government officials on local air quality, and the mechanism through which this effect takes place, using data from 282 prefecture‐level cities in China from 2015 to 2019. This research shows that turnover of local government officials led to a decline in local air quality, mainly due to a “responsibility gap period.” It also found that the heterogeneity of officials and the difference in the implementation of political initiatives by cities affected the length of the “responsibility gap period” – the shorter it was, the lower the air pollution was during official turnover. Air pollution in China is still severe and the Chinese central government should therefore further reform the turnover system to improve local air quality during the turnover of key local officials by shortening the “responsibility gap period.”

How does new-type urbanization affect air pollution? Empirical evidence based on spatial spillover effect and spatial Durbin model.

To alleviate the ecological and environmental problems caused by rapid urban development, China has formulated and implemented the new-type urbanization strategy. However, there is insufficient empirical research on the specific relationship between new-type urbanization and air pollution. Therefore, based on the panel data of 30 provinces in China from 2005 to 2018, this paper constructs a comprehensive evaluation index system of new-type urbanization using the five dimensions of population, economy, space, society, and green. The spatial Durbin model and the spatial mediating model are used to discuss the spatial effect, transmission mechanism, and regional heterogeneity of new-type urbanization on air pollution. The results show that China's air pollution mainly presents a spatial pattern of high-high agglomeration and low-low agglomeration, and there are spatial fluctuations. The construction of new-type urbanization significantly reduces local air pollution, and the industrial structure optimization, technological innovation, and energy structure adjustment are considered as important transmission mechanisms. However, under the fiscal decentralization and political tournament system in China, the policy implementation deviation may weaken the emission reduction effect of new-type urbanization, which is not conducive to regional environmental governance. From the sub-regional level, the impact of new-type urbanization on air pollution has regional heterogeneity. A robustness test confirms the reliability of our research conclusions. This study also proposes some policy suggestions that the government can utilize in grasping the policy focus of new-type urbanization construction to discover effective ways of controlling air pollution.

Health benefits by attaining the new WHO air quality guideline targets in China: A nationwide analysis

To reduce the high disease burden caused by air pollution, World Health Organization (WHO) issued a new air quality guideline (AQG) on the 22nd September 2021. A timely quantitative assessment of health benefits by meeting these targets is a key measure to advocate and inform national and regional disease control policies. We collected daily major air pollution data in 315 Chinese cities from the 1st January to the 31st December 2019, and the corresponding annual population and mortality rate in the whole population of each city. Then, the mortality benefits were estimated when daily air pollution levels attained WHO's new AQG targets (15 μg/m3 for PM2.5, 25 μg/m3 for NO2 and 100 μg/m3 for O3) in 315 Chinese cities and 31 provinces by using pollutant- and cause-specific concentration-response functions. In total, 134,025 (95%CI: 92,768; 173,029) air pollution-associated non-accidental deaths could be avoided in 315 Chinese cities in 2019 by attaining WHO's new AQG targets, with 43,800 (95%CI: 29,945; 55,616) avoidable deaths from PM2.5, 58,070 (95%CI: 45,333; 70,714) from NO2, and 32,155 (95%CI: 17,490; 46,699) from O3. Cardiovascular diseases and respiratory diseases accounted for 72,698 (95%CI: 46,561; 101,680) and 17,726 (95%CI: 8603; 26,925) avoidable deaths, respectively. Health benefits from reduction in air pollution levels were 99.26 avoided non-accidental deaths per million population at national level, ranging from 12.48 per million in Tibet to 166.26 per million in Hebei. These findings suggest that the compliance with the WHO updated AQG standards would save substantial amount of air pollution-related premature deaths in China. More stringent air pollution control and management measures are urgently warranted to reduce the disease burden from air pollutants in China, particularly for the worsening O3 pollution.

Associations of pregnancy complications with ambient air pollution in China

BackgroundGestational hypertension (GH), preeclampsia (PE), and gestational diabetes mellitus (GDM) are common pregnancy complications and can result in maternal and prenatal morbidity and mortality. Air pollution exposure could adversely impact pregnancy complications; however, evidence remains limited in China, where ambient air pollution is relatively severe. ObjectiveThis study aims to examine the associations of GH, PE, and GDM with exposure to six air pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) during pregnancy. MethodsLeveraging a multicenter birth cohort study among pregnant women in 24 hospitals from 15 provinces in China, we obtained data for maternal characteristics and pregnancy outcomes. We generated ambient concentrations of the six air pollutants using a combination of chemical transport model simulations with monitoring data. We used multivariable logistic regression models to estimate the effects on pregnancy complications from exposure to six air pollutants in each trimester and the entire pregnancy. ResultsAmong the total 3754 pregnant women in this study, the prevalences of GH, GDM, and PE were 2.6 %, 11.2 %, and 0.7 %, respectively. GH risk increased 11.9 % (95 % CI, −8.5 %, 36.8 %) and 13.8 % (1.4 %, 27.8 %) per 10 μg/m3 increases in PM2.5 and PM10 in the entire pregnancy, respectively. PM2.5 and PM10 exposures in the first trimester were significantly associated with an increased risk of GDM. Exposure to O3, SO2, NO2, and CO in early pregnancy could be associated with GDM risk. Geographic region and season of conception may influence the associations of GH and PE with air pollution. ConclusionsAmbient particulate matter pollution adversely affects GH, GDM, and PE among Chinese pregnant women. Since most regions of China still suffer from hazardous levels of air pollution, our findings indicate importance of better protecting pregnant women from the risk of air pollution.

The effect of air pollution and emotional and behavioral problems on preschoolers' overweight and obesity.

Childhood overweight and obesity (OWO) has risen dramatically in both developed and developing countries over the past few decades, creating a huge burden of disease. Ambient air pollution and emotional and behavioral problems are important influencing factors of OWO in preschoolers, but few studies have evaluated the impact of air pollution and emotional and behavioral problems on OWO of preschoolers in rural areas and their potential interactions. This study selected 3802 preschool children from 26 kindergartens in 4 rural areas of Anhui Province for a cross-sectional study. A total of 3636 individuals were included in the final analysis. In this study, outdoor air pollutants (PM2.5 and O3) were derived from the China Air Pollution Tracking (TAP) data set, matching preschoolers' external air pollution exposure according to their kindergarten address codes to neighborhoods or administrative villages. OWO were assessed based on WHO Child Growth and Development Standards. Generalized linear model (GLM) and interplot model were used to evaluate the separate effects and potential interactions of air pollutants and emotional and behavioral problems on preschoolers' OWO. In the separate analysis, we found a significant positive association between air pollution and emotional and behavioral problems and OWO among preschoolers. In the interaction analysis, air pollution could enhance the positive effect of emotional and behavioral problems on OWO in preschoolers. In addition, the effect of air pollution and emotional and behavioral problems on overweight and obesity was stronger in preschoolers aged 5 to 6years. Finally, we also found a stronger positive association between emotional and behavioral problems among girls, macrosomia, non-left-behind children, and preschoolers without eating problems. This study provided a scientific basis for the control of air pollution and overweight and obesity among preschool children in Anhui Province.

Analysis of air quality spatial spillover effect caused by transportation infrastructure

Road traffic has changed as an essential source of influential urban air pollution in China. This study developed the Spatial Durbin Model to analyze the spatial spillover effects on air pollution caused by urban transportation infrastructure. The results show that high-emission areas congregate in the eastern region of China, which reflects noticeable spatial spillover effects and agglomeration characteristics as high-emission “clubs.” When the traffic construction11Traffic construction area refer to the acreage of street area within the city in this paper. area increases by 1%, the air pollution level will increase 3.2% as accompanied. The improvement of the transportation infrastructure will bring out economic development. The speed of air pollution generated by the resources consumed by economic development is higher than that of technological improvement in the economic transition period to reduce air pollution. In contrast, economic growth and air pollution exacerbation represent a U-shaped curve connection. Most provinces remain in a stage where air pollution worsened with economic growth.

Correlation between biomass burning and air pollution in China: Spatial heterogeneity and corresponding factors

Biomass burning (i.e., forest fires or open-field burning) is strongly correlated with regional air pollution. However, the correlation varied in different regions because of the complex interaction with other driving factors, leaving a substantial gap in understanding the mechanism of air pollution formation. This study aimed to clarify the spatial heterogeneity and corresponding factors of the biomass burning-air pollution correlation in China in recent decades (2001–2019). Multisource datasets, including satellite observations, in situ measurements, and annual statistics, were adopted for the evaluation through mathematical statistics and spatial-temporal analysis. The results showed that biomass burning mainly occurred in Northeast, Central and South China, which decreased slightly by −153.08 km 2 ·yr −1 during the study period. Under this background, air quality improved significantly, particularly after 2013. The strongest correlations occurred in Northeast China at the spatial scale or in spring at the temporal scale. Notably, the correlation was affected by several factors and their complex interactions. Specifically, the dominant factors of the correlations were gross domestic product (GDP) in Northeast China, atmospheric humidity in Central China, and population in South China. In addition, these factors exerted significant interactions, which were characterized by the two-factor enhancement of temperature-GDP in Northeast China, the nonlinear enhancement of temperature-DEM in Central China, and the nonlinear enhancement of temperature-population in South China. The results of this study were helpful for capturing the physical correlation between biomass burning and air pollution, which would further support relevant air pollution management and control. • Biomass burning impact on air pollution were evaluated in pixel-wise. • Strong correlations located in Northeast, Central and South China. • Dominant factors were GDP, humidity and population in three hotspots regions. • Interactions had a stronger effect on correlation than single factors.

The Extraordinary Trend of the Spatial Distribution of PM2.5 Concentration and Its Meteorological Causes in Sichuan Basin

Sichuan Basin is an area with some of the most serious PM2.5 pollution, and it is also a key area for joint prevention and control of air pollution in China. Therefore, it is necessary to clarify the temporal and spatial distribution characteristics of PM2.5 concentration in Sichuan Basin (SCB) and study the influence of meteorological conditions. In this study, the spatial disparity of PM2.5 concentration in SCB and its variation trend from 1 December 2015 to 30 November 2019 were analyzed. The results showed that the spatial disparity of SCB was decreasing and distinct variation trends of PM2.5 concentration were observed in different areas. The PM2.5 concentrations declined rapidly in the western and southern basin (most severely polluted areas), decreased at a slower rate in the central and eastern basin, but unexpectedly increased slightly in the northern and northeastern basin. From the perspective of relative spatial anomalies (RAs), the decreasing (increasing) trend of RAs of PM2.5 concentrations in the western and southern (northern and northeastern) parts of SCB were also prominent. The reduction in spatial disparity and the regionally extraordinary increasing trend could be partly explained by the variations in synoptic circulations. Specifically, the reasons for the decrease in wintertime spatial disparity and the increase in RAs in the northern basin were the reduction in synoptic pattern Type 2 (weak high-pressure system and uniform pressure fields) and Type 3 (high-pressure system to the north) and the growth of Type 6 (weak low-pressure system with high-pressure system to the north). In spring, the reasons were the reduction in Type 1 (weak low-pressure system) and Type 5 (weak low-pressure system to the southwest) and the growth of Type 2. The reduction in Type 2 and the growth in Type 4 (weak high-pressure system to the east) could explain the variation in PM2.5 distribution in autumn. This study showed the importance of implementing more precise and effective emission control measures, especially in relatively cleaner areas, in which the impacts of meteorological conditions might cause fluctuation (even rebounding) in the PM2.5 concentration.

Prediction of Fine Particulate Matter Concentration near the Ground in North China from Multivariable Remote Sensing Data Based on MIV-BP Neural Network

Rapid urbanization and industrialization lead to severe air pollution in China, threatening public health. However, it is challenging to understand the pollutants’ spatial distributions by relying on a network of ground-based monitoring instruments, considering the incomplete dataset. To predict the spatial distribution of fine-mode particulate matter (PM2.5) pollution near the surface, we established models based on the back propagation (BP) neural network for PM2.5 mass concentration in North China using remote sensing products. According to our predictions, PM2.5 mass concentrations are affected by changes in surface reflectance and the dominant particle size for different seasons. The PM2.5 mass concentration predicted by the seasonal model shows a similar spatial pattern (high in the east but low in the west) influenced by the terrain, but shows high value in winter and low in summer. Compared to the ground-based data, our predictions agree with the spatial distribution of PM2.5 mass concentrations, with a mean bias of +17% in the North China Plain in 2017. Furthermore, the correlation coefficients (R) of the four seasons’ instantaneous measurements are always above 0.7, indicating that the seasonal models primarily improve the PM2.5 mass concentration prediction.

Trends in secondary inorganic aerosol pollution in China and its responses to emission controls of precursors in wintertime

Abstract. The Chinese government recently proposed ammonia (NH3) emission reductions (but without a specific national target) as a strategic option to mitigate fine particulate matter (PM2.5) pollution. We combined a meta-analysis of nationwide measurements and air quality modeling to identify efficiency gains by striking a balance between controlling NH3 and acid gas (SO2 and NOx) emissions. We found that PM2.5 concentrations decreased from 2000 to 2019, but annual mean PM2.5 concentrations still exceeded 35 µg m−3 at 74 % of 1498 monitoring sites during 2015–2019. The concentration of PM2.5 and its components were significantly higher (16 %–195 %) on hazy days than on non-hazy days. Compared with mean values of other components, this difference was more significant for the secondary inorganic ions SO42-, NO3-, and NH4+ (average increase 98 %). While sulfate concentrations significantly decreased over this period, no significant change was observed for nitrate and ammonium concentrations. Model simulations indicate that the effectiveness of a 50 % NH3 emission reduction for controlling secondary inorganic aerosol (SIA) concentrations decreased from 2010 to 2017 in four megacity clusters of eastern China, simulated for the month of January under fixed meteorological conditions (2010). Although the effectiveness further declined in 2020 for simulations including the natural experiment of substantial reductions in acid gas emissions during the COVID-19 pandemic, the resulting reductions in SIA concentrations were on average 20.8 % lower than those in 2017. In addition, the reduction in SIA concentrations in 2017 was greater for 50 % acid gas reductions than for the 50 % NH3 emission reductions. Our findings indicate that persistent secondary inorganic aerosol pollution in China is limited by emissions of acid gases, while an additional control of NH3 emissions would become more important as reductions of SO2 and NOx emissions progress.

Exploring the causal relationship between urbanization and air pollution: Evidence from China

• Urbanization has both negative and positive effects on air pollution. • The effects of urbanization on PM 2.5 concentrations could last for 6–8 years. • PM 2.5 concentrations have decoupled from urbanization since 2015 in China. • Different stages of urbanization have divergent impacts on air pollution. Urbanization and air quality are both closely linked to sustainable development and human well-being. However, few studies discussed the improvement effect, stage characteristics, and time-lag effects of urbanization on air quality. It is not clear whether China's air pollution has decoupled from urbanization. Based on the population, economic, land, and social dimensions, we analyzed the relationship between China's urbanization and air pollution using fully modified least squares, Granger causality test, impulse response functions, and variance decomposition. The results show that urbanization has both negative and positive effects on air quality, which is related to the urbanization stages and policies. From 2000 to 2012, each 1% increase in population, economic, land, and social urbanization increased PM 2.5 concentration by -0.28%, 0.71%, 0.04%, and 0.16%, respectively. However, from 2013 to 2019, the effects were mostly not significant. The increasing urbanization rate could reduce PM 2.5 concentrations during the entire period. Bi-directional long-term causality between population, economic, and land urbanization and PM 2.5 concentration existed before 2013, after which causality became uni-directional, and the effects of PM 2.5 concentrations on urbanization weakened. The impact of urbanization could last for 6–8 years. PM 2.5 concentrations were decoupled from all dimensions of urbanization after 2015 in China. This study provides a useful reference for government decision-making and also gives insights and methods to other studies.

A benefit allocation model for the joint prevention and control of air pollution in China: In view of environmental justice

The benefit allocation of fair and justice is an inevitable guarantee for the long-term operation of the joint prevention and control of air pollution (JPCAP). The ignorance of interest demands of various governance subjects in the existing benefit allocation mechanism results in the widespread “free rider” behavior in the joint control and unsatisfactory effects of JPCAP. Given this, it is imperative to build a reasonable benefit allocation model. The innovation of this paper is proposing a benefit allocation model of JPCAP to achieve the symmetry between control costs and benefits based on environmental justice. The control objectives and total benefits of JPCAP are calculated through the adjustment of optimal removal rates. The interest demands of various control subjects and benefit compensation scheme are clarified by adopting an improved Shapley method, which comprehensively considers factors affecting environmental justice. An empirical analysis is conducted on SO2 governance in Beijing-Tianjin-Hebei (BTH) and its surrounding areas. The results show that the benefit allocation model based on environmental justice can not only accurately evaluate the benefits of joint control, but also effectively achieve the symmetry between control costs and benefits. This study provides a scientific and reasonable theoretical basis for the benefit allocation of SO2 control and can be extended to the researches and practices of other air pollutants control.

Evaluation of Long-Term Modeling Fine Particulate Matter and Ozone in China During 2013–2019

Air quality in China has been undergoing significant changes due to the implementation of extensive emission control measures since 2013. Many observational and modeling studies investigated the formation mechanisms of fine particulate matter (PM2.5) and ozone (O3) pollution in the major regions of China. To improve understanding of the driving forces for the changes in PM2.5 and O3 in China, a nationwide air quality modeling study was conducted from 2013 to 2019 using the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modeling system. In this study, the model predictions were evaluated using the observation data for the key pollutants including O3, sulfur dioxide (SO2), nitrogen dioxide (NO2), and PM2.5 and its major components. The evaluation mainly focused on five major regions, that is , the North China Plain (NCP), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), the Chengyu Basin (CY), and the Fenwei Plain (FW). The CMAQ model successfully reproduced the air pollutants in all the regions with model performance indices meeting the suggested benchmarks. However, over-prediction of PM2.5 was noted in CY. NO2, O3, and PM2.5 were well simulated in the north compared to the south. Nitrate (NO3−) and ammonium (NH4+) were the most important PM2.5 components in heavily polluted regions. For the performance on different pollution levels, the model generally over-predicted the clean days but underpredicted the polluted days. O3 was found increasing each year, while other pollutants gradually reduced during 2013–2019 across the five regions. In all of the regions except PRD (all seasons) and YRD (spring and summer), the correlations between PM2.5 and O3 were negative during all four seasons. Low-to-medium correlations were noted between the simulated PM2.5 and NO2, while strong and positive correlations were established between PM2.5 and SO2 during all four seasons across the five regions. This study validates the ability of the CMAQ model in simulating air pollution in China over a long period and provides insights for designing effective emission control strategies across China.

Does Ambient Secondary Conversion or the Prolonged Fast Conversion in Combustion Plumes Cause Severe PM2.5 Air Pollution in China?

The ambient formation of secondary particulate matter (ambient FSPM) is commonly recognized as the major cause of severe PM2.5 air pollution in China. We present observational evidence showing that the ambient FSPM was too weak to yield a detectable contribution to extreme PM2.5 pollution events that swept northern China between 11 and 14 January 2019. Although the Community Multiscale Air Quality (CMAQ) model (v5.2) reasonably reproduced the observations in January 2019, it largely underestimated the concentrations of the PM2.5 during the episode. We propose a novel mechanism, called the “in-fresh-stack-plume non-precipitation-cloud processing of aerosols” followed by the evaporation of semi-volatile components from the aerosols, to generate PM2.5 at extremely high concentrations because of highly concentrated gaseous precursors and large amounts of water droplets in fresh cooling combustion plumes under poor dispersion conditions, low ambient temperature, and high relative humidity. The recorded non-precipitation-cloud processing of the aerosols in fresh stack combustion plumes normally lasts 20–30 s, but it prolongs as long as 2–5 min under cold, humid, and stagnant meteorological conditions and expectedly causes severe PM2.5 pollution events. Regardless of the presence of the natural cloud in the planetary boundary layer during the extreme events, the fast conversion of air pollutants in water droplets and the generation of the PM2.5 through the non-precipitation-cloud processing of aerosols always occur in fresh combustion plumes. The processing of aerosols is detectable using a nano-scan particle sizer assembled on an unmanned aerial vehicle to monitor the particle formation in stack plumes. In-fresh-stack-plume processed aerosols under varying meteorological conditions need to be studied urgently.

Association of Long-term Ambient Fine Particulate Matter (PM2.5) and Incident CKD: A Prospective Cohort Study in China

Increasing evidence has linked ambient fine particulate matter (ie, particulate matterno larger than 2.5μm [PM2.5]) to chronic kidney disease (CKD), but their association has not been fully elucidated, especially in regions with high levels of PM2.5 pollution. This study aimed to investigate the long-term association of high PM2.5 exposure with incident CKD in mainland China. Prospective cohort study. 72,425 participants (age≥18 years) without CKD were recruited from 121 counties in Hunan Province, China. Annual mean PM2.5 concentration at the residence of each participant derived from a long-term, full-coverage, high-resolution (1× 1 km2), high-quality dataset of ground-level air pollutants in China. Incident CKD during the interval between the baseline examination of each participant (2005-2017) and the end of follow-up through2018. Cox proportional hazards models were used to estimate the independent association of PM2.5 with incident CKD and the joint association of PM2.5 with temperature or humidity on the development of PM2.5-related CKD. Restricted cubic splines were used to model exposure-response relationships. Over a median follow-up of 3.79 (IQR, 2.03-5.48) years, a total of 2,188 participants with incident CKD were identified. PM2.5 exposure was associated with incident CKD with an adjusted hazard ratio of 1.71 (95% CI, 1.58-1.85) per 10-μg/m3 greater long-term exposure. Multiplicative interactions between PM2.5 and humidity or temperature on incident CKD were detected (all P < 0.001 for interaction), whereas an additive interaction was detected only for humidity (relative risk due to interaction, 3.59 [95% CI, 0.97-6.21]). Lack of information on participants' activity patterns such as time spent outdoors. Greater long-term ambient PM2.5 pollution is associated with incident CKD in environments with high PM2.5 exposure. Ambient humidity has a potentially synergetic effect on the association of PM2.5 with the development of CKD. Exposure to a form of air pollution known as fine particulate matter (ie, particulate matter≤2.5 μm [PM2.5]) has been linked to an increased risk of chronic kidney disease (CKD), but little is known about how PM2.5 affects CKD in regions with extremely high levels of PM2.5 pollution. This longitudinal cohort study in China investigates the effect of PM2.5 on the incidence of CKD and whether temperature or humidity interact with PM2.5. Our findings suggest that long-term exposure to high levels of ambient PM2.5 significantly increased the risk of CKD in mainland China, especially in terms of cumulative average PM2.5. The associations of PM2.5 and incident CKD were greater in high-humidity environments. These findings support the recommendation that reducing PM2.5 pollution should be a priority to decrease the burden of associated health risks, including CKD.

Heavy Metal, Waste, COVID-19, and Rapid Industrialization in This Modern Era—Fit for Sustainable Future

Heavy metal contamination, waste, and COVID-19 are hazardous to all living things in the environment. This review examined the effects of heavy metals, waste, and COVID-19 on the ecosystem. Scientists and researchers are currently working on ways to extract valuable metals from waste and wastewater. We prefer Tessier sequential extraction for future use for heavy metal pollution in soil. Results indicated that population growth is another source of pollution in the environment. Heavy metal pollution wreaks havoc on soil and groundwater, especially in China. COVID-19 has pros and cons. The COVID-19 epidemic has reduced air pollution in China and caused a significant reduction in CO2 releases globally due to the lockdown but has a harmful effect on human health and the economy. Moreover, COVID-19 brings a huge amount of biomedical waste. COVID-19’s biomedical waste appears to be causing different health issues. On the other hand, it was discovered that recycling has become a new source of pollution in south China. Furthermore, heavy metal contamination is the most severe ecological effect. Likewise, every problem has a remedy to create new waste management and pollution monitoring policy. The construction of a modern recycling refinery is an important aspect of national waste disposal.