Understanding sources of fine particulate matter in China

Exposure to low concentrations of PM2.5 and its constituents with preterm birth in Shenzhen, China: a retrospective cohort study

BACKGROUND

Due to the Air Pollution Prevention and Control Measures issued by the Chinese government, air quality has significantly improved, particularly with respect to PM2.5. However, studies on the relationship between low concentrations of PM2.5 and preterm birth (PTB) remain limited in China.

OBJECTIVE

To examine the associations between low concentrations of PM2.5 and its constituents and PTB.

METHODS

This retrospective cohort study was conducted from July 2021 to April 2023 in Shenzhen, China. Data on questionnaires and pregnancy outcomes were collected for each participant. Using the Tracking Air Pollution in China (TAP) dataset, we assessed the concentrations of PM2.5 and its chemical constituents, including sulfate (SO42-), nitrate (NO3-), organic matter (OM), black carbon (BC), and ammonium (NH4+). We applied a generalized additive model (GAM) to evaluate the relationship. The relationship between exposure to PM2.5 and its constituents and PTB was further examined using a method that combined dummy variable settings with trend tests. Stratified analysis was conducted to explore the potential factors.

RESULTS

Among 17,240 live-born infants, the rate of PTB was 6.0%, and the average exposure concentration of PM2.5 was 20.24 μg/m3. There were positive associations between PM2.5 and its constituents and PTB. With each interquartile range (IQR) increase in PM2.5 during the third trimester, the risk of PTB increased by 2.23 times. The exposure effects of sulfate (SO42-) and organic matter (OM) were comparable to the total PM2.5. The third trimester might be the critical susceptibility window. The risk was higher among women who conceived in the cold season and were exposed to higher temperatures during pregnancy.

CONCLUSION

Even at low levels, PM2.5 can still increase the risk of PTB, with varying health effects attributed to different constituents. This underscores the importance of further strengthening environmental management and characterizing the contributions of PM2.5 sources.

Phytoremediation potential of potted plant species against vehicular emissions

Urbanization and industrialization are exponentially deteriorating air quality, ecosystems, and human health. Phytoremediation is cost cost-effective, sustainable, and nature-based solution against air pollution. This study is designed to evaluate four species, Chlorophytum comosum, Rhapis excelsa, Spathiphyllum wallisii, and Ficus benjamina for their phytoremediation potential. The experimental setup consisted of a sealed chamber to place potted plants and equipment, it was also connected to the vehicular exhaust pipe. The Air Pollution Tolerance Index was highest for F. benjamina (12.19) and lowest for Rhapis excels (8.58). C. comosum has the highest VOC removal efficiency (90%, 0.172 ppm h-1). NOx remediation was highest by F. benjamina with 0.057 ppm h-1 (77%) removal efficiency. SOx and CO were remediated more efficiently by C. comosum, as 89%, (0.18 ppm h-1) and 80% (0.23 ppm h-1), respectively. R. excelsa reduced a higher concentration of NH3 (77%, 0.06 ppm h-1) compared to other species. R. excelsa and S. wallisii may serve as bio-indicator species. These findings provide a sustainable, natural, economical, and eco-friendly way to mitigate air pollution. F. benjamina and C. comosum are suitable species for urban landscapes, green spaces, urban plantations, and green walls to curb air pollutants due to traffic and industries.

Projecting non-communicable diseases attributable to air pollution in the climate change era: a systematic review

OBJECTIVES

Climate change is a major global issue with significant consequences, including effects on air quality and human well-being. This review investigated the projection of non-communicable diseases (NCDs) attributable to air pollution under different climate change scenarios.

DESIGN

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 flow checklist. A population-exposure-outcome framework was established. Population referred to the general global population of all ages, the exposure of interest was air pollution and its projection, and the outcome was the occurrence of NCDs attributable to air pollution and burden of disease (BoD) based on the health indices of mortality, morbidity, disability-adjusted life years, years of life lost and years lived with disability.

DATA SOURCES

The Web of Science, Ovid MEDLINE and EBSCOhost databases were searched for articles published from 2005 to 2023.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

The eligible articles were evaluated using the modified scale of a checklist for assessing the quality of ecological studies.

DATA EXTRACTION AND SYNTHESIS

Two reviewers searched, screened and selected the included studies independently using standardised methods. The risk of bias was assessed using the modified scale of a checklist for ecological studies. The results were summarised based on the projection of the BoD of NCDs attributable to air pollution.

RESULTS

This review included 11 studies from various countries. Most studies specifically investigated various air pollutants, specifically particulate matter <2.5 µm (PM2.5), nitrogen oxides and ozone. The studies used coupled-air quality and climate modelling approaches, and mainly projected health effects using the concentration-response function model. The NCDs attributable to air pollution included cardiovascular disease (CVD), respiratory disease, stroke, ischaemic heart disease, coronary heart disease and lower respiratory infections. Notably, the BoD of NCDs attributable to air pollution was projected to decrease in a scenario that promotes reduced air pollution, carbon emissions and land use and sustainable socioeconomics. Contrastingly, the BoD of NCDs was projected to increase in a scenario involving increasing population numbers, social deprivation and an ageing population.

CONCLUSION

The included studies widely reported increased premature mortality, CVD and respiratory disease attributable to PM2.5. Future NCD projection studies should consider emission and population changes in projecting the BoD of NCDs attributable to air pollution in the climate change era.

PROSPERO REGISTRATION NUMBER

CRD42023435288.

Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques

The mining industry confronts significant challenges in mitigating airborne particulate matter (PM) pollution, necessitating innovative approaches for effective monitoring and prediction. This research focuses on the design and development of an Internet of Things (IoT)-based real-time monitoring system tailored for PM pollutants in surface mines, specifically PM 1.0, PM 2.5, PM 4.0, and PM 10.0. The novelty of this work lies in the integration of IoT technology for real-time measurement and the application of machine learning (ML) techniques for accurate prediction based on recorded dust pollutants data. The study's findings indicate that PM 1.0 pollutants exhibited the highest concentration in the atmosphere of the ball clay surface mine sites, with the stockyard site registering the maximum levels of PM pollutants (28.45 µg/m3, 27.89 µg/m3, 26.17 µg/m3, and 27.24 µg/m3, respectively) due to the dry nature of clay materials. Additionally, the research establishes four ML models-Decision Tree (DT), Gradient Boosting Regression (GBR), Random Forest (RF), and Linear Regression (LR)-for predicting PM pollutant concentrations. Notably, Random Forest demonstrates superior performance with the lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) at 1.079 and 1.497, respectively. This comprehensive solution, combining IoT-based monitoring and ML-based prediction, contributes to sustainable mining practices, safeguarding worker well-being, and preserving the environment.

Outdoor Health Risk of Atmospheric Particulate Matter at Night in Xi'an, Northwestern China

The deterioration of air quality via anthropogenic activities during the night period has been deemed a serious concern among the scientific community. Thereby, we explored the outdoor particulate matter (PM) concentration and the contributions from various sources during the day and night in winter and spring 2021 in a megacity, northwestern China. The results revealed that the changes in chemical compositions of PM and sources (motor vehicles, industrial emissions, coal combustion) at night lead to substantial PM toxicity, oxidative potential (OP), and OP/PM per unit mass, indicating high oxidative toxicity and exposure risk at nighttime. Furthermore, higher environmentally persistent free radical (EPFR) concentration and its significant correlation with OP were observed, suggesting that EPFRs cause reactive oxygen species (ROS) formation. Moreover, the noncarcinogenic and carcinogenic risks were systematically explained and spatialized to children and adults, highlighting intensified hotspots to epidemiological researchers. This better understanding of day-night-based PM formation pathways and their hazardous impact will assist to guide measures to diminish the toxicity of PM and reduce the disease led by air pollution.

Recent Progress in Atmospheric Chemistry Research in China: Establishing a Theoretical Framework for the "Air Pollution Complex"

Atmospheric chemistry research has been growing rapidly in China in the last 25 years since the concept of the "air pollution complex" was first proposed by Professor Xiaoyan TANG in 1997. For papers published in 2021 on air pollution (only papers included in the Web of Science Core Collection database were considered), more than 24 000 papers were authored or co-authored by scientists working in China. In this paper, we review a limited number of representative and significant studies on atmospheric chemistry in China in the last few years, including studies on (1) sources and emission inventories, (2) atmospheric chemical processes, (3) interactions of air pollution with meteorology, weather and climate, (4) interactions between the biosphere and atmosphere, and (5) data assimilation. The intention was not to provide a complete review of all progress made in the last few years, but rather to serve as a starting point for learning more about atmospheric chemistry research in China. The advances reviewed in this paper have enabled a theoretical framework for the air pollution complex to be established, provided robust scientific support to highly successful air pollution control policies in China, and created great opportunities in education, training, and career development for many graduate students and young scientists. This paper further highlights that developing and low-income countries that are heavily affected by air pollution can benefit from these research advances, whilst at the same time acknowledging that many challenges and opportunities still remain in atmospheric chemistry research in China, to hopefully be addressed over the next few decades.

The Motion Behavior of Micron Fly-Ash Particles Impacting on the Liquid Surface

The motion behavior of particles impacting on the liquid surface can affect the capture efficiency of particles. It was found that there are three kinds of motion behaviors after particle impact on the liquid surface: sinking, rebound, and oscillation. In this paper, the processes of micron fly-ash particles impacting on the liquid surface were experimentally studied under normal temperature and pressure. The impact of fly-ash particles on the liquid surface was simulated by a dynamic model. Based on force analysis, the dynamic model was developed and verified by experimental data to distinguish between three motion behaviors. Then, the sinking/rebound critical velocity and rebound/oscillation critical velocity were calculated by the dynamic model. The critical velocities of particles impacting on the liquid surface under different particle sizes, receding angles, and surface tension coefficients were analyzed. As the particle size increased, sinking/rebound critical velocity and rebound/oscillation critical velocity decreased. As the receding angle increased, sinking/rebound critical velocity remained unchanged, and the rebound/oscillation critical velocity decreased. As the liquid surface tension coefficient increased, sinking/rebound critical velocity and rebound/oscillation critical velocity increased. On this basis, the behaviors of particles impacting on the liquid at low velocity were analyzed.

Pollution characteristics and sources of environmentally persistent free radicals and oxidation potential in fine particulate matter related to city lockdown (CLD) in Xi'an, China

The impact of COVID-19 control on air quality have been prevalent for the past two years, however few studies have explored the toxicity of atmospheric particulate matter during the epidemic control. Therefore, this research highlights the characteristics and sources of oxidative potential (OP) and the new health risk substances environmentally persistent free radicals (EPFRs) in comparison to city lockdown (CLD) with early days of 2019-2020. Daily particulate matter (PM2.5) samples were collected from January 14 to February 3, 2020, with the same period during 2019 in Xi'an city. The results indicated that the average concentration of PM2.5 decreased by 48% during CLD. Concentrations of other air pollutants and components, such as PM10, NO2, SO2, WSIs, OC and EC were also decreased by 22%, 19%, 2%, 17%, 6%, and 4% respectively during the CLD, compared to the same period in 2019. Whereas only O3 increased by 30% during CLD. The concentrations of EPFRs in PM2.5 was considerably lower than in 2019, which decreased by 12% during CLD. However, the OP level was increased slightly during CLD. Moreover, both EPFRs/PM and DTTv/PM did not decrease or even increase significantly, manifesting that the toxicity of particulate matter has not been reduced by more gains during the CLD. Based on PMF analysis, during the epidemic period, the contribution of traffic emission is significantly reduced, while EPFRs and DTTv increased, which consist of significant O3 and secondary aerosols. This research leads to able future research on human health effect of EPFRs and oxidative potential and can be also used to formulate the majors to control EPFRs and OP emissions, suggest the need for further studies on the secondary processing of EPFRs and OP during the lockdown period in Xi'an. .The COVID-19 lockdown had a significant impact on both social and economic aspects. The city lockdown, however, had a positive impact on the environment and improved air quality, however, no significant health benefits were observed in Xi'an, China.

Abating ammonia is more cost-effective than nitrogen oxides for mitigating PM2.5 air pollution

[Figure: see text].

A chronology of global air quality

Air pollution has been recognized as a threat to human health since the time of Hippocrates, ca 400 BC. Successive written accounts of air pollution occur in different countries through the following two millennia until measurements, from the eighteenth century onwards, show the growing scale of poor air quality in urban centres and close to industry, and the chemical characteristics of the gases and particulate matter. The industrial revolution accelerated both the magnitude of emissions of the primary pollutants and the geographical spread of contributing countries as highly polluted cities became the defining issue, culminating with the great smog of London in 1952. Europe and North America dominated emissions and suffered the majority of adverse effects until the latter decades of the twentieth century, by which time the transboundary issues of acid rain, forest decline and ground-level ozone became the main environmental and political air quality issues. As controls on emissions of sulfur and nitrogen oxides (SO₂ and NO_x) began to take effect in Europe and North America, emissions in East and South Asia grew strongly and dominated global emissions by the early years of the twenty-first century. The effects of air quality on human health had also returned to the top of the priorities by 2000 as new epidemiological evidence emerged. By this time, extensive networks of surface measurements and satellite remote sensing provided global measurements of both primary and secondary pollutants. Global emissions of SO₂ and NO_x peaked, respectively, in ca 1990 and 2018 and have since declined to 2020 as a result of widespread emission controls. By contrast, with a lack of actions to abate ammonia, global emissions have continued to grow. This article is part of a discussion meeting issue 'Air quality, past present and future'.

Monitoring of PM2.5 Concentrations by Learning from Multi-Weather Sensors

This paper aims to monitor the ambient level of particulate matter less than 2.5 μm (PM2.5) by learning from multi-weather sensors. Over the past decade, China has established a high-density network of automatic weather stations. In contrast, the number of PM monitors is much smaller than the number of weather stations. Since the haze process is closely related to the variation of meteorological parameters, it is possible and promising to calculate the concentration of PM2.5 by studying the data from weather sensors. Here, we use three machine learning methods, namely multivariate linear regression, multivariate nonlinear regression, and neural network, in order to monitor PM2.5 by exploring the data of multi-weather sensors. The results show that the multivariate linear regression method has the root mean square error (RMSE) of 24.6756 μg/m3 with a correlation coefficient of 0.6281, by referring to the ground truth of PM2.5 time series data; and the multivariate nonlinear regression method has the RMSE of 24.9191 μg/m3 with a correlation coefficient of 0.6184, while the neural network based method has the best performance, of which the RMSE of PM2.5 estimates is 15.6391 μg/m3 with the correlation coefficient of 0.8701.