Decrease In PM2 Research Articles

Estimating the effect of air quality on Bike-Sharing usage in Shanghai, China: An instrumental variable approach

Recent years have seen a surge in shared mobility. Amid various emerging shared mobility services, bike-sharing has received growing popularity for its economic affordability. Meanwhile, the “green” reputation of bike-sharing has also been widely acknowledged by the general public. The two-way causal relationship between environmental quality and bike-sharing posits a virtuous circle that environmental quality improvement leads to increasing bike-sharing demand, and more bike-sharing trips contribute to better environmental quality. While numerous efforts have been made to explore the environmental benefits of bike-sharing systems, relevant studies examining the impacts of environmental quality on bike-sharing behaviors are lacking. Given its importance in transportation and environmental sustainability, this study aims to examine the impacts of air quality on bike-sharing demand in Shanghai, China, in August 2016 through a fixed-effect two-stage least square (FE-2SLS) model, where wind speed and thermal inversion are innovatively adopted as the instrumental variables. The findings show that a unit decrease in PM2.5 concentrations is associated with a 2,517 increase in daily bike-sharing trips in Shanghai. Alternatively, an additional 0.78 million bike-sharing trips per month could be generated during summer seasons if Shanghai’s summer-time PM2.5 levels could meet WHO’s interim target of 10 μg/m3. Due to substantial economic, environmental, and public health benefits arising from bike-sharing trips and air quality improvements, our findings provide compelling evidence for policymakers to devote continued efforts to air pollution control and increased investments in bike-sharing systems.

Statistical significance of PM2.5 and O3 trends in China under long-term memory effects

Over the past decade, the Chinese government has implemented the “Clean Air Action” measures to enhance the atmospheric environmental quality, primarily focusing on curbing PM2.5 and O3 concentrations. The efficacy of these strategies and the underlying causes (human factors or natural variability) of any observed increases or decreases in PM2.5 and O3 concentrations are of great importance. Examining the hourly PM2.5 and O3 concentration time series from six representative regions in China between 2015 and 2021 revealed an overall downward trend in PM2.5 concentrations. However, the O3 concentration time series indicated upward trends in some regions, except for the Northeast area (NE) and Sichuan Basin (SCB). In the context of conventional significance tests, the assumption is typically that the time series' samples are independent and therefore memoryless. However, in situations where the time series exhibits strong autocorrelation and limited sample size, this assumption can lead to an overestimation of the statistical significance of the linear trend. To account for this, we utilized a long-term memory model that can reproduce the long-term persistence of pollutant records to improve the accuracy of significance tests. By comparing the P-values of real and surrogate data generated by the long-term memory model, we found that only PM2.5 concentrations in the Pearl River Delta (PRD) were slightly insignificant. For the remaining five regions, the P-values of PM2.5 concentrations were smaller than the significant level of 0.05, suggesting that the observed downward trends in PM2.5 concentrations are not due to natural variability, thereby confirming the effectiveness of the government's policies aimed at curbing atmospheric particulate matter in recent years. Our results show that O3 pollution is significantly increasing only in the Beijing-Tianjin-Hebei (BTH) region, beyond natural variability. In contrast, the trends of O3 pollution in many regions of China are markedly impacted by natural and climate variability.

An Inappropriate Rise in NO2 During the COVID-19 Pandemic in the Urban Area of Chhattisgarh, India

The COVID-19 first case was reported in India on January 30, 2020, and in Chhattisgarh on March 19, 2020, and since then a sharp surge has been noticed. The government of India imposed a nationwide lockdown on March 25, 2020, a complete suspension of human activities, viz., industry, construction, transport, academic institutions, etc., which resulted in an improvement in air quality (a decrease in PM2.5 and PM10, alteration in NO2 and an increase in O3). But the rate of cases of COVID-19 has increased sharply, and eventually, under economic pressure, the lockdown was withdrawn on June 1, 2020, which further accelerated the exponential growth of COVID-19 cases. We noticed that in the Chhattisgarh State of India, the alteration in the air quality index during the lockdown period is continuing even after the restoration of anthropogenic activities. Among PM2.5, PM10, NO2, and O3, the behavior of NO2 was found to be different than others; it was found to increase during the lockdown period but further decreased with the resumption of anthropogenic activities. We conclude that the air quality index has an insignificant impact on COVID-19 infection.

Response of PM2.5 pollution to meteorological and anthropogenic emissions changes during COVID-19 lockdown in Hunan Province based on WRF-Chem model

In December 2019, the New Crown Pneumonia (the COVID-19) outbroke around the globe, and China imposed a nationwide lockdown starting as early as January 23, 2020. This decision has significantly impacted China's air quality, especially the sharp decrease in PM2.5 (aerodynamic equivalent diameter of particulate matter less than or equal to 2.5 μm) pollution. Hunan Province is located in the central and eastern part of China, with a “horseshoe basin” topography. The reduction rate of PM2.5 concentrations in Hunan province during the COVID-19 (24.8%) was significantly higher than the national average (20.3%). Through the analysis of the changing character and pollution sources of haze pollution events in Hunan Province, more scientific countermeasures can be provided for the government. We use the Weather Research and Forecasting with Chemistry (WRF-Chem, V4.0) model to predict and simulate the PM2.5 concentrations under seven scenarios before the lockdown (2020.1.1–2020.1.22) and during the lockdown (2020.1.23–2020.2.14). Then, the PM2.5 concentrations under different conditions is compared to differentiate the contribution of meteorological conditions and local human activities to PM2.5 pollution. The results indicate the most important cause of PM2.5 pollution reduction is anthropogenic emissions from the residential sector, followed by the industrial sector, while the influence of meteorological factors contribute only 0.5% to PM2.5. The explanation is that emission reductions from the residential sector contribute the most to the reduction of seven primary contaminants. Finally, we trace the source and transport path of the air mass in Hunan Province through the Concentration Weight Trajectory Analysis (CWT). We found that the external input of PM2.5 in Hunan Province is mainly from the air mass transported from the northeast, accounting for 28.6%–30.0%. To improve future air quality, there is an urgent need to burn clean energy, improve the industrial structure, rationalize energy use, and strengthen cross-regional air pollution synergy control.

Predicting the Impact of Change in Air Quality Patterns Due to COVID-19 Lockdown Policies in Multiple Urban Cities of Henan: A Deep Learning Approach

Several countries implemented prevention and control measures in response to the 2019 new coronavirus virus (COVID-19) pandemic. To study the impact of the lockdown due to COVID-19 on multiple cities, this study utilized data from 18 cities of Henan to understand the air quality pattern change during COVID-19 from 2019 to 2021. It examined the temporal and spatial distribution impact. This study firstly utilized a deep learning bi-directional long-term short-term (Bi-LSTM) model to predict air quality patterns during 3 periods, i.e., COVID-A (before COVID-19, i.e., 2019), COVID-B (during COVID-19, i.e., 2020), COVID-C (after COVID-19 cases, i.e., 2021) and obtained the R2 value of more than 72% average in each year and decreased MAE value, which was better than other studies’ deep learning methods. This study secondly focused on the change of pollutants and observed an increase in Air Quality Index by 10%, a decrease in PM2.5 by 14%, PM10 by 18%, NO2 by 14%, and SO2 by 16% during the COVID-B period. This study found an increase in O3 by 31% during the COVID-C period and observed a significant decrease in pollutants during the COVID-C period (PM10 by 42%, PM2.5 by 97%, NO2 by 89%, SO2 by 36%, CO by 58%, O3 by 31%). Lastly, the impact of lockdown policies was studied during the COVID-B period and the results showed that Henan achieved the Grade I standards of air quality standards after lockdown was implemented. Although there were many severe effects of the COVID-19 pandemic on human health and the global economy, lockdowns likely resulted in significant short-term health advantages owing to reduced air pollution and significantly improved ambient air quality. Following COVID-19, the government must take action to address the environmental problems that contributed to the deteriorating air quality.

Investigating the Impact of Shifting the Brick Kiln Industry from Conventional to Zigzag Technology for a Sustainable Environment

The brick kiln industry is one of the largest and most highly unregulated industrial sectors in developing countries. Most of the kilns use low-quality coal as primary fuel along with small quantities of bagasse, rice husk, and wooden chips. As a result of inefficient methods of combustion in conventional brick kilns, such as fixed chimney Bull’s trench kilns (FCBTKs), harmful pollutants are emitted in high quantities, which ultimately deteriorate the environment and are widely in operation in Pakistan. The most prominent harmful pollutants include carbon dioxide (CO2), carbon monoxide (CO), sulphur dioxide (SO2), black carbon (BC), and particulate matter less than 2.5 microns (PM2.5). Over the years, new technologies have been adopted by developed countries for the reduction of environmental burdens. One of these technologies is induced draught zigzag kilns (IDZKs), or zigzag kilns (ZZKs), technology, which effectively improves the combustion across the path of bricks stacked in a zigzag pattern. For the mass adoption of this technology, environmental assessment and comparison of both technologies is a crucial step. Both types of kiln sites are investigated for the analysis of their emissions and their environmental impact in this work. Carbon mass balance equations are used for the calculation of emission factors. Collected inventory data is then used for the life cycle assessment of both types of kilns using open LCA (version 1.10.3) and the Eco-invent database. According to the study, ZZK technology outperforms FCBTK in all aspects. The analysis of the specific energy consumption (SEC) of fired bricks for each kiln type reveals that ZZKs require 30% less energy than the conventional FCBTK. This implies that ZZKs demand lesser fuel than FCBTKs. The zigzag technology adoption scenario, in particular, can lead to approximately 30% lower CO2 emissions, which can be further reduced by up to 80% when taking into account black carbon (BC) emissions. Additionally, the adoption of zigzag technology can result in a 35% decrease in PM2.5 emissions. The study shows that adopting ZZK technology significantly reduces impact categories, such as particulate matter formation (PMF), photochemical oxidant formation (POF), and terrestrial acidification (TA) by 63%, 93%, and 95%, respectively.

Pollution Characteristics and Factors Influencing the Reduction in Ambient PM2.5 in Beijing from 2018 to 2020

Air quality data from 33 environment sites and five regional sites from 2018 to 2020, as well as meteorological data, were used to research PM2.5 variation,spatial and temporal change, diurnal variation, and heavy pollutions in Beijing. The annual average mass concentrations of PM2.5 in Beijing were 51, 42, and 38 μg·m-3, which showed great progress in air quality improvement. However, the PM2.5 concentration in 2020 was still 8.6% above the national limit value despite a 30.9% decline since 2017. The PM2.5 south-north gradient in Beijing remained throughout the three years, but this pattern showed a less significant trend. The highest monthly mean PM2.5 concentrations in Beijing tended to occur in January-March, with the lowest in August-September. NOx, CO, and PM2.5 concentrations were significantly higher in the heating season than in the non-heating season by 58.4%, 52.9%, and 27.5%, respectively. Diurnal variation showed that greater PM2.5 concentrations were observed at nighttime during the heating season and, conversely, at noontime during the non-heating season. Sixteen pollution episodes occurred in Beijing over the last three years, resulting in 25 heavy pollution days distributed in autumn-winter of 2018-2020. The regional heavy pollution characteristics of PM2.5 in Beijing were significant. Through analysis, a continuous pollution reduction was still the most important reason for the yearly decrease in PM2.5. The concentrations of organic matter, elemental carbon, and crustal matter in the PM2.5 in Beijing decreased by 43.3%, 53.2%, and 51.5% since 2017, respectively, and nitrate, sulfate, and ammonium decreased by 34.2%, 52.2%, and 43.7%.The results showed that the control effect of PM2.5 in Beijing was obvious.

Investigation on the relationship between the number of coronavirus disease 2019 cases at the beginning of the epidemic and the decrease of PM2.5 in Hubei, China: The role of temperature changes

In response to the coronavirus disease 2019 pandemic, the Chinese government implemented blockade measures in Hubei, which largely affected the emission of pollutants. This work is aimed to explore the effects of epidemics on pollutants at different temperatures in Hubei, China. We applied for a panel nonlinear model with autonomous search thresholds to explore this, using daily average temperature as a threshold variable, and PM2.5 set as the explained variable, and the cumulative number of confirmed coronavirus disease 2019 cases set as the explanatory variable. An empirical analysis was conducted by running the proposed model and using nine cities in China most impacted by the pandemic. The results show that there was a non-linear negative relationship between the cumulative number of confirmed coronavirus disease 2019 cases and PM2.5. A more detailed non-linear relationship between the two was uncovered by the proposed panel threshold regression model. When the temperature crosses the threshold value (12.5 °C and 20.5 °C) in sequence, the estimated value was −0.0688, −0.0934, and −0.1520 in that order. This means that this negative non-linear relationship increased with increasing temperature. This work helps to explore the effect of coronavirus disease 2019 on pollutions at different temperatures and provides a methodological reference to study their nonlinear relationship.

Factors affecting recent PM2.5 concentrations in China and South Korea from 2016 to 2020

This study used observational data and a chemical transport model to investigate the contributions of several factors to the recent change in air quality in China and South Korea from 2016 to 2020. We focused on observational data analysis, which could reflect the annual trend of emission reduction and adjust existing emission amounts to apply it into a chemical transport model. The observation data showed that the particulate matter (PM2.5) concentrations during winter 2020 decreased by −23.4 % (−14.68 μg/m3) and − 19.5 % (−5.73 μg/m3) in China and South Korea respectively, compared with that during winter 2016. Meteorological changes, the existing national plan for a long-term emission reduction target, and unexpected events (i.e., Coronavirus disease 2019 (COVID-19) in China and South Korea and the newly introduced special winter countermeasures in South Korea from 2020) are considered major factors that may affect the recent change in air quality. The impact of different meteorological conditions on PM2.5 concentrations was assessed by conducting model simulations by fixing the emission amounts; the results indicated changes of +7.6 % (+4.77 μg/m3) and + 9.7 % (+2.87 μg/m3) in China and South Korea, respectively, during winter 2020 compared to that during winter 2016. Due to the existing and pre-defined long-term emission control policies implemented in both countries, PM2.5 concentration significantly decreased from winter 2016–2020 in China (−26.0 %; −16.32 μg/m3) and South Korea (−9.1 %; −2.69 μg/m3). The unexpected COVID-19 outbreak caused the PM2.5 concentrations in China to decrease during winter 2020 by another −5.0 % (−3.13 μg/m3). In South Korea, the winter season special reduction policy, which was introduced and implemented in winter 2020, and the COVID-19 pandemic may have contributed to −19.5 % (−5.92 μg/m3) decrease in PM2.5 concentrations.

Excess Global Blood Pressure Associated With Fine Particulate Matter Air Pollution Levels Exceeding World Health Organization Guidelines

Excess Global Blood Pressure Associated With Fine Particulate Matter Air Pollution Levels Exceeding World Health Organization Guidelines

The path towards sustainable finance: Venture capital and air pollution in China

Financing sustainable growth has attracted global attention and discussion in recent years. This study investigates the effect of venture capital, as a potential source of sustainable finance, on air pollution in China from 2003 to 2016. Using the unique Government Guidance Fund as instrumental variable, we find that venture capital activities have significantly reduced local air pollution in cities of China. To be specific, a one standard deviation increase in the VC amount leads to 4% decrease in PM2.5 concentration and 6% reduction in industrial SO2 emission. The effects are insensitive to a wide range of robustness tests. Cities characterized by more rigorous environmental supervision, superior business environments, and stronger innovation incentives benefit more from venture capital activities. We further show that venture capital helps improve local air quality by boosting general and green innovation, increasing the investment of new green enterprises, as well as crowding out the investment of polluting industries.

Evaluation of Changes in PM2.5 Exposure Concentration and Health Risk for Urban Resident in Zhengzhou Based on High Spatial Resolution Grids

In recent years, complex air pollution with the characteristic pollutant of PM2.5 has remained serious in China. Long term exposure to PM2.5 might harm residential health and can increase premature death from specific diseases. The annual average concentration of PM2.5 in Zhengzhou was much higher than the national secondary standard, which has an extremely negative impact on the health of residents. Based on the high spatial resolution grids of population density established through web-crawling and outdoor monitoring concentrations and urban residential emissions used to evaluate PM2.5 exposure concentration, the exposure concentration of PM2.5 for urban residents of Zhengzhou was assessed, considering both indoor and outdoor exposures. Relevant health risks were quantified with the integrated exposure-response model. Finally, the contributions of various reducing measures and different standards of air quality to the decreases in PM2.5 exposure concentration were analyzed. The results showed that in 2017 and 2019, the time weighted exposure concentrations of PM2.5 for Zhengzhou's urban residents were 74.06 μg·m-3 and 60.64 μg·m-3, respectively, which was decreased by 18.12%. In addition, the mass fractions of the indoor exposure concentrations in the time weighted exposure concentrations were 83.58% and 83.01%, and its contribution to the drop of the time weighted exposure concentrations was 84.06%. In 2017 and 2019, the numbers of premature deaths attributed to PM2.5 exposures for urban residents of Zhengzhou over the age of 25 were 13285 and 10323, respectively, showing a 22.30% decrease. By using these comprehensive measures, PM2.5 exposure concentration for Zhengzhou's urban residents could be reduced by 86.23% at most, and 8902 premature deaths could be avoided.

Tracing the sources of PM2.5-related health burden in China

Fine particulate matter (PM2.5) poses a major environmental risk to human health. We estimated PM2.5-related premature deaths in 30 Chinese provinces in 2020 using an integrated exposure response model based on monitored concentrations and obtained regional and sectoral contributions based on the atmospheric transport of the atmospheric transport contribution matrix. From the perspective of regional- and sectoral-scale effects, the results revealed that 740,140 [95% confidence interval (CI):646,538–839,968] premature deaths were related to PM2.5 in 2020, mainly in East (30%), Central (18%), and North (15%) China. Manufacturing activity was found to be the major cause of PM2.5-related premature deaths, accounting for over 50% of the deaths. From the perspective of the interregional atmospheric transport effect, although local emissions were the major source of PM2.5-related premature deaths in all regions, non-local emissions contributed approximately 30%. The overall trend in the net atmospheric transport direction was from north to south. In particular, the Guangdong, Guangxi, and Hainan provinces of South China received contributions of more than 40% from non-local provinces, mainly from the East and Central China. Combined with economic data, the regions and sectors with the highest PM2.5-related premature deaths per unit output or consumption include the manufacturing and household sectors in North and Northeast China and transportation, agriculture, and electricity in Central China. Therefore, from the perspective of the above three impacts, although the potential impact of PM2.5 pollution on health in China has decreased with the decrease in PM2.5 concentration in the past decade owing to strict air pollution control, the central and northern parts of China are still the key areas requiring air pollution control. The health impacts of air pollution associated with the rapid development of China's manufacturing industry in the post-pandemic era cannot be ignored.

Long-term trends and sensitivities of PM2.5 pH and aerosol liquid water to chemical composition changes and meteorological parameters in Hong Kong, South China: Insights from 10-year records from three urban sites

Aerosol pH and aerosol liquid water (ALW) play key roles in regulating many atmospheric processes. In many countries, large reductions in SO2 and NOx emissions as a result of air pollution regulatory policies have led to improved air quality and significant changes in aerosol mass and chemical composition. Understanding the long-term trends and sensitivities of aerosol pH and ALW to changes in meteorological conditions and chemical composition is useful for predicting how atmospheric processes that depend on aerosol acidity and/or ALW will evolve as air pollutant emissions change over time. At present, long-term monitoring records of aerosol pH and ALW are limited in most parts of China and elsewhere in Asia due to the scarcity of long-term aerosol chemical speciation measurements acquired through regular sampling schedules and consistent analysis protocols in this region. We used a 10-year dataset (2011–2020) from three long-term monitoring sites to investigate the long-term trends of PM2.5 pH and ALW concentrations, identify key factors that drive the variations of PM2.5 pH and ALW concentrations, and investigate the sensitivities of PM2.5 pH and ALW concentrations to chemical composition changes during different seasons in Hong Kong, a highly populated urbanized city located in South China that has experienced improved air quality as a result of various effective air pollution regulatory policies. Despite the substantial decreases in PM2.5 concentrations and changes in chemical composition in this 10-year period, PM2.5 pH showed little change. The rates at which PM2.5 pH and ALW concentration changed ranged from −0.007 to −0.001 pH units/year and from −1.45 to −0.53 μg m−3/year, respectively. Clear and consistent seasonal oscillatory cycles were observed for both PM2.5 pH (∼2–∼4) and ALW concentrations (∼5–∼40 μg m−3) at the three sites during this 10-year period. PM2.5 was the most acidic in the fall, and was the least acidic in winter. ALW concentrations were the lowest in summer, and the highest in winter/spring. Sensitivity tests revealed that chemical composition, especially particulate sulfate concentrations, plays a major role in driving the variations in aerosol pH and ALW concentration during each season. However, the magnitudes to which aerosol pH and ALW concentration respond to changes in chemical composition depend strongly on the meteorological conditions in each season. Given that many urbanized cities in South China share similar air quality and meteorological characteristics as Hong Kong, the results presented here are useful for advancing our understanding of how air pollution regulatory policies can alter aerosol pH and ALW concentrations, and consequently aqueous aerosol chemistry, in other South China cities.

Source Contributions to PM2.5-Related Mortality and Costs: Evidence for Emission Allocation and Compensation Strategies in China

The emissions from various pollution sources were not proportional to their contributions to ambient PM2.5 concentrations and associated health burdens. That means even with the same total abatement targets, different abatement allocation strategies across emission sources can have distinct health benefits. Insufficient knowledge of various sources' contributions to health burdens in China, the country suffering substantial PM2.5-related deaths, hindered the government from seeking optimized abatement allocation strategies. In this context, we separated the contributions of 155 emission sources (31 provinces × 5 sectors) to PM2.5-related mortality across China in 2017 by coupling the Comprehensive Air Quality Model with Extensions (CAMx), Weather Research and Forecasting model (WRF), and health impact assessment model. We further identified the priority-control emission sources and quantified interprovincial ecological compensation volumes to alleviate inequality induced by emission allocation strategies. Results showed that PM2.5 pollution caused 899,443 excess deaths and around 127 billion USD costs in 2017. Approximately half of the deaths and costs were attributable to emissions from sources outside the boundary of the regions where the deaths occurred. Twenty-five out of 155 emission sources that contributed to the top 60% mortality burdens and had high marginal abatement efficiencies in China shall be the priority-control emission sources. A 1 μg/m3 decrease of PM2.5 concentration in regions where these key emission sources occur shall be compensated by 76-153 million USD in their receptor regions. Our study sheds light on the sources' contributions to mortality burdens and costs and provides scientific evidence for optimizing the emission allocation and compensation strategies in China. It also has wide implications for other countries suffering similar problems.

Trends of source apportioned PM2.5 in Tianjin over 2013–2019: Impacts of Clean Air Actions

A long-term (2013–2019) PM2.5 speciation dataset measured in Tianjin, the largest industrial city in northern China, was analyzed with dispersion normalized positive matrix factorization (DN-PMF). The trends of source apportioned PM2.5 were used to assess the effectiveness of source-specific control policies and measures in support of the two China's Clean Air Actions implemented nationwide in 2013–2017 and 2018–2020, respectively. Eight sources were resolved from the DN-PMF analysis: coal combustion (CC), biomass burning (BB), vehicular emissions, dust, steelmaking and galvanizing emissions, a mixed sulfate-rich factor and secondary nitrate. After adjustment for meteorological fluctuations, a substantial improvement in PM2.5 air quality was observed in Tianjin with decreases in PM2.5 at an annual rate of 6.6%/y. PM2.5 from CC decreased by 4.1%/y. The reductions in SO2 concentration, PM2.5 contributed by CC, and sulfate demonstrated the improved control of CC-related emissions and fuel quality. Policies aimed at eliminating winter-heating pollution have had substantial success as shown by reduced heating-related SO2, CC, and sulfate from 2013 to 2019. The two industrial source types showed sharp drops after the 2013 mandated controls went into effect to phaseout outdated iron/steel production and enforce tighter emission standards for these industries. BB reduced significantly by 2016 and remained low due to the no open field burning policy. Vehicular emissions and road/soil dust declined over the Action's first phase followed by positive upward trends, showing that further emission controls are needed. Nitrate concentrations remained constant although NOX emissions dropped significantly. The lack of a decrease in nitrate may result from increased ammonia emissions from enhanced vehicular NOX controls. The port and shipping emissions were evident implying their impacts on coastal air quality. These results affirm the effectiveness of the Clean Air Actions in reducing primary anthropogenic emissions. However, further emission reductions are needed to meet global health-based air quality standards.

Impact of mitigation measures to improve home indoor air quality in Kathmandu, Nepal

Air purifiers (APs) and home sealing are interventions used to help protect U.S. diplomats against particle pollution in the home when working in polluted cities. We investigated the effect of these interventions on home indoor and personal PM2.5 exposure in Kathmandu, Nepal. Twenty-one participants underwent repeated 48 hour personal monitoring before and after intervention. We analyzed these measurements by microenvironment. Indoor-outdoor ratios (I/O) using the home indoor PM2.5 values were calculated in order to assess the air filtration capacity at home in light of increasing outdoor PM2.5 post-intervention. To quantify the effect of intervention on home indoor PM2.5, we conducted a meta-analysis of the results of dwelling-by-dwelling regression of indoor-on-outdoor (I/O) PM2.5 concentrations. On average, adding high-capacity APs and home sealing led to a 15% decrease in PM2.5 measured at home, excluding cooking periods, with a mean (standard deviation) of 7.5 (6.4) μg m−3 pre- to 6.4 (8.1) μg m−3 post-intervention despite a 57% increase in outdoor PM2.5, from 43.8 (30.8) μg m−3 pre- to 68.9 (40.7) μg m−3 post-intervention. Overall mean personal exposure fell by 36% from 15.2 (10.6) μg m−3 to 9.8 (8.7) μg m−3. I/O ratios decreased as outdoor PM2.5 strata increased; when outdoor PM2.5 < 25 μg m−3 the I/O decreased from 0.38 pre- to 0.12 post-intervention and when outdoor PM2.5 was 101–200 μg m−3 the I/O decreased from 0.12 pre- to 0.07 post-intervention. The mean regression slope of indoor-on-outdoor PM2.5 decreased from 0.13 (95% CI 0.09, 0.17) in pre-intervention dwellings to 0.07 (0.04, 0.10) post-intervention. I/O ratios showed a weak negative (not statistically significant) inverse association with air changes per hour at home. In the high pollution environment of Kathmandu, APs with home sealing provide substantial protection against ambient PM2.5 in the home environment, including during periods when outdoor PM2.5 concentration was above 100 μg m−3.

Variations and possible causes of the December PM2.5 in Eastern China during 2000–2020

High air pollutions of PM2.5 concentrations have become a serious environmental problem in China during recent decades, causing significant influences on urban air quality and human health. In the study, we investigate the variations of the December PM2.5 in Eastern China and the possible causes during 2000–2020. The empirical orthogonal function (EOF) analysis is employed to reveal the dominant patterns of PM2.5 variability in Eastern China. The EOF1 shows a consistent variability in the whole of the Eastern China, which reflects a consistent emission pattern in Eastern China in past two decades. The EOF2 exhibits a North-South dipole pattern, which is closely tied to the changes of atmospheric circulations. The increase of PM2.5 in the North Eastern China is mainly related to the decrease of wind speed, the decrease of boundary layer height and the increase of inversion temperature, while the decrease of PM2.5 in the South Eastern China is affected by the increase of local precipitation. Two atmospheric wave trains are identified that affect the dipole distribution of PM2.5 in Eastern China. The southern one is affected by ENSO, and the northern one is jointly affected by ENSO, sea surface temperature of Labrador Sea and sea ice concentration near Kara Sea. Finally, we reconstructed a comprehensive atmospheric external forcing index based on these factors. We find that the comprehensive index can well reproduce the North-South dipole distribution of PM2.5 in Eastern China, indicating the plausible effects of the atmospheric external forcings and the prediction potential for the variations of PM2.5 in Eastern China.

Impacts of Emission and Meteorological Conditions on Air Pollutants at Various Sites Around the COVID-19 Lockdown in Wuhan

The random forest algorithm was used to separate the mass concentrations of six air pollutants (SO2, NO2, CO, PM10, PM2.5, and O3) contributed by emissions and meteorological conditions. Their variations for five types of sites including Wuhan's central urban, suburb, industrial, the third ring road traffic, and urban background sites were investigated. The results showed that the values of PM2.5/CO, PM10/CO, and NO2/CO during the lockdown period decreased by 10.8-21.7, 9.34-24.7, and 14.4-22.1 times compared with the period before the lockdown, indicating that the contributions of emissions to PM2.5, PM10, and NO2 were reduced. O3/CO increased by 50.1-61.5 times, implying that the secondary formation increased obviously. The contributions of emissions to various types of pollutants all increased after the lockdown. During the lockdown period, affected by the operation of some uninterrupted industrial processes, PM2.5 concentrations in industrial areas dropped the least (20.5%). Compared with the lockdown period, residential activities, transportation, and industrial production were basically restored after the lockdown, resulting in the alleviation of the reduction in PM2.5 emission-related concentrations. The increase in emission-related O3 concentrations could be associated with the decreased NO and PM2.5 concentrations during the lockdown period. The elevated O3 partially offset the improved air quality brought by the reduced NO2and PM2.5 concentrations. After the lockdown, ρ(O3) related with meteorology at the suburban and urban background sites increased by 16.2 μg·m-3 and 16.1 μg·m-3, respectively, which could be attributed to the increased ambient temperature and decreased relative humidity. The decrease in PM2.5 and increase in O3 concentrations caused by reduced traffic and industrial emissions at the third ring road traffic and central urban regions can provide reference for the current coordinated and precise control of PM2.5 and O3 in subregions.

Classification and sources of extremely severe sandstorms mixed with haze pollution in Beijing

Air quality has significantly improved in China; however, new challenges emerge when dust weather is combined with haze pollution during spring in northern China. On March 15, 2021, an extremely severe sandstorm occurred in Beijing, with hourly maximum PM10 and PM2.5 concentrations reaching 5267.7 μg m−3 and 963.9 μg m−3, respectively. Continuous sandstorm events usually lead to complicated pollution status in spring. Three pollution types were identified disregarding the time sequence throughout March. The secondary formation type was dominant, with high ratios of PM2.5/PM10 (mean 74%) and PM1/PM2.5 (mean 52%). This suggests that secondary transformations are the primary cause of heavy pollution, even during the dry seasons. Sandstorm type resulted in dramatic PM10 levels, with a noticeable decrease in PM2.5/PM10 levels (27%), although PM2.5 levels remain high. The transitional pollution type was distinguished by an independent increase in PM10 levels, although PM2.5 and PM1 levels differed from the PM10 levels. Throughout March, the sulfur oxidation rate varied considerably, with high levels during most periods (mean 0.52). A strong correlation indicated that relative humidity was the primary variable promoting the formation of secondary sulfate. Sandstorms promote heterogeneous reactions by providing abundant reaction surfaces from mineral particles, therefore aggravating secondary pollution. The sandstorm air mass from the northwest passing through the sand sources of Mongolia carried not only crustal matter but also organic components, such as bioaerosols, resulting in a sharp increase in the organic carbon in PM2.5.