Annual Average PM2 Research Articles

Effect measure modification of the association between short-term exposures to PM2.5 and hospitalizations by long-term PM2.5 exposure among a cohort of people with Chronic Obstructive Pulmonary Disease (COPD) in North Carolina, 2002–2015

BackgroundApproximately nine million adults in the United States are living with chronic obstructive pulmonary disease (COPD), and positive associations between short-term air pollution exposure and increased risk of COPD hospitalizations in older adults are consistently reported. We examined the association between short-term PM2.5 exposure and hospitalizations and assessed if there is modification by long-term exposure in a cohort of individuals with COPD.MethodsIn a time-referent case-crossover design, we used a cohort of randomly selected individuals with electronic health records from the University of North Carolina Healthcare System, restricted to patients with a medical encounter coded with a COPD diagnosis from 2004–2016 (n = 520), and estimated ambient PM2.5 concentrations from an ensemble model. Odds ratios and 95% confidence intervals (OR (95%CI)) were estimated with conditional logistic regression for respiratory-related, cardiovascular (CVD), and all-cause hospitalizations. Exposures examined were 0–2 and 0–3 day lags of PM2.5 concentration, adjusting for daily census-tract temperature and humidity, and models were stratified by long-term (annual average) PM2.5 concentration at the median value.ResultsWe observed generally null or low-magnitude negative associations with short-term PM2.5 exposure and respiratory-related (OR per 5 µg/m3 increase in 3-day lag PM2.5: 0.971 (0.885, 1.066)), CVD (2-day lag: 0.976 (0.900, 1.058) and all-cause (3 day lag: 1.003 (0.927, 1.086)) hospitalizations. Associations between short-term PM2.5 exposure and hospitalizations were higher among patients residing in areas with higher levels of annual PM2.5 concentrations (OR per 5 µg/m3 in 3-day lag PM2.5 for all-cause hospitalizations: 1.066 (0.958, 1.185)) than those in areas with lower annual PM2.5 concentrations (OR per 5 µg/m3 in 3-day lag PM2.5 for all-cause hospitalizations: 0.914 (0.804, 1.039)).ConcluisonsDifferences in associations demonstrate that people in areas with higher annual PM2.5 exposure may be associated with higher risk of hospitalization during short-term increases in PM2.5 exposure.

Prediction of daily PM2.5 and ozone based on high-density weather stations in China: Nonlinear effects of meteorology, human and ecosystem health risks

In recent decade, despite the implementation of strict emission reductions, PM2.5 and surface ozone (O3) in China are still at high pollution level. In this study, based on observations at air quality stations (∼1500 mainly in urban region), daily PM2.5 and MDA8 (the daily maximum 8 h average) O3 concentrations in high-density and uniform meteorological sites (∼2000) over China during 2017–2019 were retrieved using generalized additive models (GAMs) for different regions on a seasonal scale, in order to establish 0.1° gridded datasets and investigate their spatiotemporal characteristics, nonlinear effects of meteorology, and ambient air pollution-attributable health risks on a regional scale. This model can provide high accuracy in the estimation of daily PM2.5 (O3) in 2018 with overall cross-validation R2 and RMSE of 0.81 (0.80) and 12.73 (17.63) μg/m3 and predictive capability in 2017 and 2019 with R2 of 0.67 (0.70) for PM2.5 (O3). Temperature, relative humidity and visibility are three main meteorological factors driving PM2.5 (O3) response. PM2.5 generally exhibits an exponential decrease with increasing visibility and an increase with increasing relative humidity, while is suppressed at the thresholds of 60% and 70%. For O3, in northern China, positive response of temperature is more important and O3 suppression exists at 36 °C ∼ 41.5 °C, whereas negative response of relative humidity is dominating in southern China. Overall, annual averaged PM2.5 (O3) values across China reduced (increased) by 18.7% (11.2%) between 2017 and 2019, with the highest concentrations and variation in North China. These variations contributed to a 14.9% reduction in PM2.5 attributed premature deaths, but a 10.4% increase in O3 exposure related deaths. Generally, central-eastern China still exists greater health risk with per capita premature mortality ranging from 0.80‰ to 1.19‰ for PM2.5 and 1.91‰ to 2.60‰ for O3. Additionally, O3 vegetation damages demonstrate a decline (increase) by 1.3% (21.7%) in North China with higher O3 pollution (southern China with slight pollution) between 2017 and 2019. These findings highlight the essentiality for the synergistic control of PM2.5 and O3 based on regional differences in China.

Whether the Establishment of National High-Tech Zones Can Improve Urban Air Pollution: Empirical Evidence from Prefecture-Level Cities in China

Promoting the construction of national-level high-tech zones as green pioneer zones is a necessary condition for achieving high-quality development. Based on panel data from 254 prefecture-level cities from 2006 to 2018, in this paper, the difference-in-difference (DID) is used to empirically examine the influence effect of the establishment of national high-tech zones on local urban air pollution, and the spatial difference-in-difference (SDID) is used to explore its spatial spillover effect. It was found that the annual average PM2.5 concentration in cities with national high-tech zones decreased by about 1.8% compared with cities without national high-tech zones, and there was a positive spillover effect on the annual average PM2.5 concentration in nearby cities. Industrial structure upgrading and technological innovation effects are two important transmission paths for the establishment of national high-tech zones to influence urban air pollution; the heterogeneity analysis shows that the establishment of national high-tech zones has more significant implications for the improvement of air pollution in non-resource cities and less developed areas in the west, and the air pollution improvement effect of “growing” national high-tech zones is more desirable than that of “mature” national high-tech zones. Our empirical results conclude that we should continue to encourage the promotion of national high-tech zones, optimize the business environment, improve preferential policies, and design a combination of policy instruments scientifically according to local conditions in order to give full play to the effect of national high-tech zones on urban air-pollution improvement.

Regional Representativeness Analysis of Ground-Monitoring PM2.5 Concentration Based on Satellite Remote Sensing Imagery and Machine Learning Techniques

The issue of urban air quality in China has become increasingly significant due to industrialization and rapid urbanization. Although PM2.5 is the major air pollutant in most cities of northern China and has a direct negative impact on human health, there is a problem of under-representativeness at Chinese monitoring stations. In some cities, due to the relatively fewer national control stations and the fact that the stations are located closer to pollution sources, under the current assessment system, the monitoring data are not sufficient for the fairness of air quality assessment in different cities. In this article, the multispectral data of Landsat 8 data, air quality data, and meteorological data from ground monitoring stations have been integrated together and imported to different PM2.5-estimation models established based on the multi-layer back propagation neural network (MLBPN), support vector regression (SVR), and random forest (RF), respectively. According to the evaluation indices of R2, RMSE, and ME, the estimation model based on the MLBPN revealed the best PM2.5 estimation results and was therefore employed for the regional representativeness analysis in the study area of Xi’an, Shaanxi, China. The annual average PM2.5 concentration in the study area is depicted after error correction using Kriging interpolation, which can be further used to evaluate and analyze the representativeness of monitoring stations in Xi’an. By calculating the difference between the actual station annual average and the annual average of estimated PM2.5 concentration in the whole region, it can be found that the regional annual average value of PM2.5 in Xi’an is overestimated. To sum up, this article proposes a feasible method for the spatial positioning of the air quality monitoring stations to be established.

Peningkatan Pengetahuan mengenai Bahaya dan Risiko Gangguan Pernapasan pada Pengemudi Ojek Online

Jakarta is listed as one of the cities in the world with the most polluted air quality, with an annual average PM 2.5 of 49.4 g/m³ in 2019. The sources of air pollution in Jakarta among others are the large number of motor vehicles that are not in accordance with the guidelines of safe vehicles for the environment. Not to mention air pollution from industries and factories fueled by coal or other fossils. Online motorcycle taxi drivers are a profession that is at risk of experiencing the effects of this air pollution. Respiratory disorders that can occur such as acute respiratory infections, chronic obstructive pulmonary disease (COPD), decreased lung function and lung malignancy. To reduce the risk of respiratory problems that can arise in online motorcycle taxi drivers, efforts are being made to prevent and stop exposure to air pollutants through counseling activities which carried out on November 12, 2023. This activity was attended by 42 online motorcycle taxi drivers. Participants' knowledge was assessed by pre-test and post-test using the Google form to assess participants' cognitive changes. From the results of the pre-test and post-test evaluation, it was found that there was an increase in knowledge about the risks and respiratory tract disorders that could arise, namely 25.5%, with an increasing percentage of correct answers of pre-test and post-test ranging from 4.8 % - 40.5%. It is hoped that this increased knowledge will change the behavior of online motorcycle taxi drivers to always pay attention of prevention and stop exposure to air pollutants.

How does coordinated regional digital economy development improve air quality? New evidence from the spatial simultaneous equation analysis

Countries around the world are increasingly turning towards developing digital economies to find better strategies for tackling the environmental pollution associated with economic growth while also pursuing high-quality economic conditions. This study aims to probe the link between coordinated regional digital economy development (RDEC) and air quality. A province-level RDEC indicator based on city-level data is developed, and air pollution is gauged by annual average PM2.5 concentrations. Furthermore, a spatial simultaneous equation model is employed to examine the causality further. The empirical results indicate that a bilateral causal relationship exists: RDEC improves air quality, and better air quality also facilitates RDEC. This relationship is influenced by spatial spillover effects. Specifically, air quality and RDEC of an area have a negative influence on the RDEC of neighboring regions, while they have a positive impact on neighboring areas' air quality. Further analysis suggests that green total factor productivity, advanced industrial structure, and regional entrepreneurship level can indirectly affect the contribution of RDEC to air quality. Additionally, the impact of air quality on RDEC may be realized through the increase in labor productivity, lower external environmental costs of regional economic development, and enhanced regional foreign economic exchange.

Modelling PM10 removal in three Italian coastal Metropolitan Cities along a latitudinal gradient

Air pollution is regarded as the largest environmental risk factor in Europe, and Particulate Matter (PM) is considered one of the most harmful pollutants. Among the Ecosystem Services (ESs) it provides, the Urban Green Infrastructure (UGI) is capable of capturing and adsorbing pollutants through removal mechanisms. As envisaged in the National Plan for Recovery and Resilience (PNRR), Italy has planned on investing about EUR 300 million of the Next Generation EU funds in urban reforestation programs that will target Italian Metropolitan Cities (MCs). Successful implementation of such interventions is underpinned by a thorough knowledge of the presence and distribution of existing vegetation and its capacity to deliver ESs. In the present study, we selected three Italian coastal MCs along a latitudinal gradient, namely Genoa, Bari, and Reggio Calabria, and assessed the regulating ES of PM10 removal by urban and peri-urban forests in both biophysical and monetary terms. In 2019, the total PM10 removal in the MCs of Genoa, Bari and Reggio Calabria amounted to 5,331, 363, and 4,248 Mg respectively, for a corresponding monetary value of EUR 536, 39, and 435 million. Our analyses were conducted on a seasonal basis and further aimed at investigating the role of functional diversity in ES provision. According to our findings, in all MCs the highest annual average PM10 removal efficiencies were exhibited by the evergreen broadleaves; deciduous broadleaves also displayed high efficiency values, despite being characterized by a marked seasonality caused by leaf abscission. Conifers proved less efficient, although our findings may indicate a good response to a condition of multi-stress. Maintaining a functionally-mixed species composition in the UGIs is desirable for guaranteeing the continuous provision of ESs throughout the year. In conclusion, our results provide useful insights to support the sustainable planning and management of UGIs, as envisaged by the United Nation's 2030 Agenda for Sustainable Development.

Spatio-Temporal Change Pattern Investigation of PM2.5 in Jiangsu Province with MODIS Time Series Products

In the last decade, the spatio-temporal patterns of PM2.5 on various scales, ranging from global, continent, and country to regional levels, has been the focus of considerable studies. However, these studies on spatio-temporal variability have concentrated primarily on changes in the spatial distribution patterns of regional PM2.5 concentrations and ignored temporal characteristics at a local site from a heterogeneous surface, such as local variability, persistence, and stability of PM2.5 exposure. Understanding the temporal characteristics of PM2.5 concentration changes at the local scale will help determine the local impacts of PM2.5, such as local exposure risk and vulnerability to PM2.5. This study aims to reveal the local characteristics of temporal variation at the scale of a prefecture-level city and its distinct-varying patterns from those at the provincial scale by using the annual satellite-derived PM2.5 concentration product from 2000 to 2015. The evolutionary trends, stability, and persistence of annual changes were discovered with a set of time series analysis methods, such as linear regression analysis + F-test, coefficient of variation method, and Hurst index. This study uses PM2.5 product data for a total of 16 years, from 2000 to 2015, and uses time series analysis methods, such as Theil–Sen median trend analysis + Mann–Kendall test, one-dimensional linear regression analysis + F-test, coefficient of variation method, and Hurst index, to reveal the temporal variation characteristics and spatial patterns of PM2.5 in Jiangsu Province. The results show that the increasing trends or slopes of annual averaged PM2.5 concentrations in Jiangsu Province are not consistent at the prefecture-level city scale, but they are consistent in northern, central and southern Jiangsu at a larger upward trend since 2000. The areas with significant increasing trends are concentrated in Xuzhou and Lianyungang and other northern cities. From the viewpoint of variability, the areas in medium and high variability are mainly aggregated in the areas north of the Yangtze River. According to the combination of persistence analysis and trend analysis, future variation in PM2.5 concentrations indicates an inverse persistence for an increasing trend, meaning the air quality decline in Jiangsu will slow.

Long-term exposure to ambient fine particulate matter and carotid intima media thickness at bilateral, left and right in adults from Mexico City: Results from GEA study

BackgroundPM2.5 exposure has been associated with intima-media thickness (cIMT) increase. However, very few studies distinguished between left and right cIMT in relation to PM2.5 exposure. AimTo evaluate associations between chronic exposure to PM2.5 and cIMT at bilateral, left, and right in adults from Mexico City. MethodsThis study comprised 913 participants from the control group, participants without personal or family history of cardiovascular disease, of the Genetics of Atherosclerosis Disease Mexican study (GEA acronym in Spanish), recruited at the Instituto Nacional de Cardiología Ignacio Chávez from June 2008 to January 2013. To assess the associations between chronic exposure to PM2.5 (per 5 μg/m3 increase) at different lag years (1–4 years) and cIMT (bilateral, left, and right) we applied distributed lag non-linear models (DLNMs). ResultsThe median and interquartile range for cIMT at bilateral, left, and right, were 630 (555, 735), 640 (550, 750), and 620 (530, 720) μm, respectively. Annual average PM2.5 exposure was 26.64 μg/m3, with median and IQR, of 24.46 (23.5–25.46) μg/m3. Results from DLNMs adjusted for age, sex, body mass index, low-density lipoproteins, and glucose, showed that PM2.5 exposure for year 1 and 2, were positively and significantly associated with right-cIMT [6.99% (95% CI: 3.67; 10.42) and 2.98% (0.03; 6.01), respectively]. Negative associations were observed for PM2.5 at year 3 and 4 and right-cIMT; however only year 3 was statistically significant [-2.83% (95% CI: 5.12; −0.50)]. Left-cIMT was not associated with PM2.5 exposure at any lag year. The increase in bilateral cIMT followed a similar pattern as that observed for right-cIMT, but with lower estimates. ConclusionsOur results suggest different susceptibility between left and right cIMT associated with PM2.5 exposure highlighting the need of measuring both, left and right cIMT, regarding ambient air pollution in epidemiological studies.

Air quality characteristics during 2016–2020 in Wuhan, China

Implementation of a clean air policy in China has high national importance. Here, we analyzed tempo-spatial characteristics of the concentrations of PM2.5 (PM2.5_C), PM10 (PM10_C), SO2 (SO2 _C), NO2 (NO2 _C), CO (CO _C), and maximum 8-h average O3 (O3_8h_C), monitored at 22 stations throughout the mega-city of Wuhan from January 2016 to December 2020, and their correlations with the meteorological and socio-economic factors. PM2.5_C, PM10_C, SO2 _C, NO2 _C, and CO _C showed similar monthly and seasonal trends, with minimum value in summer and maximum value in winter. However, O3_8h_C showed an opposite monthly and seasonal change pattern. In 2020, compared to the other years, the annual average PM2.5_C, PM10_C, SO2 _C, NO2 _C, and CO _C were lower. PM2.5_C and PM10_C were higher in urban and industrial sites and lower in the control site. The SO2_C was higher in industrial sites. The NO2_C was lower, and O3_8h_C was higher in suburban sites, while CO showed no spatial differences in their concentrations. PM2.5 _C, PM10 _C, SO2 _C, NO2 _C, and CO _C had positive correlations with each other, while O3_8h_C showed more complex correlations with the other pollutants. PM2.5_C, PM10_C, SO2 _C, and CO _C presented a significantly negative association with temperature and precipitation, while O3 was significantly positively associated with temperature and negatively associated with relative air humidity. There was no significant correlation between air pollutants and wind speed. Gross domestic product, population, number of automobiles, and energy consumption play an important role in the dynamics of air quality concentrations. These all provided important information for the decision and policy-makers to effectively control the air pollution in Wuhan.

Assessment of seasonal variations in the fine particulate matter of indoor air in sub urban area of Jammu District (J&K), India

Indoor aerosol PM 2.5 is more harmful due to its penetration deep into lungs most ofpeoplespendingmore than 90% of their time indoor. The present study is the first timeinvestigation to evaluate the indoor aerosols (PM 2.5) in the households located in residential, commercial and industrial sub urban areas Jammu District (J&K) during different seasons of the two year study period (2017-2019). The indoor PM 2.5 was observed to exhibit deceasing trend i.e. more in summer> winter >rainy season. In non-wood fuel burning households exhibited annual average indoor PM 2.5 values below the values prescribed by CPCB and wood fuel burning households exhibited values above the values prescribed by CPCB. Moreover the indoor aerosols (PM 2.5 ) was observed be to almost four times higher in wood fuel burning households as compared with that of non-wood fuel burning households.

Associations between long-term fine particulate matter exposure and hospital procedures in heart failure patients.

Ambient fine particulate matter (PM2.5) contributes to global morbidity and mortality. One way to understand the health effects of PM2.5 is by examining its impact on performed hospital procedures, particularly among those with existing chronic disease. However, such studies are rare. Here, we investigated the associations between annual average PM2.5 and hospital procedures among individuals with heart failure. Using electronic health records from the University of North Carolina Healthcare System, we created a retrospective cohort of 15,979 heart failure patients who had at least one of 53 common (frequency > 10%) procedures. We used daily modeled PM2.5 at 1x1 km resolution to estimate the annual average PM2.5 at the time of heart failure diagnosis. We used quasi-Poisson models to estimate associations between PM2.5 and the number of performed hospital procedures over the follow-up period (12/31/2016 or date of death) while adjusting for age at heart failure diagnosis, race, sex, year of visit, and socioeconomic status. A 1 μg/m3 increase in annual average PM2.5 was associated with increased glycosylated hemoglobin tests (10.8%; 95% confidence interval = 6.56%, 15.1%), prothrombin time tests (15.8%; 95% confidence interval = 9.07%, 22.9%), and stress tests (6.84%; 95% confidence interval = 3.65%, 10.1%). Results were stable under multiple sensitivity analyses. These results suggest that long-term PM2.5 exposure is associated with an increased need for diagnostic testing on heart failure patients. Overall, these associations give a unique lens into patient morbidity and potential drivers of healthcare costs linked to PM2.5 exposure.

Characterization of fine particulate matter water-soluble inorganic ions and estimation of aerosol acidity at three COALESCE network sites - Mysuru, Bhopal, and Mesra - in India.

The study was carried out to understand the chemical, spatiotemporal characteristics of water-soluble inorganic ions (WSIIs), their association with PM2.5 mass, and aerosol acidity in three COALESCE (carbonaceous aerosol emissions, source apportionment, and climate impacts) network sites of India (Mesra - Eastern India, Bhopal - Central India and Mysuru - Southern India). Alternate-day 24-h integrated bulk PM2.5 samples were collected during 2019 along with on-site meteorological parameters. Annual average PM2.5 concentrations were 67 ± 46µgm-3, 54 ± 47µgm-3, and 30 ± 24µgm-3 at Mesra, Bhopal, and Mysuru, respectively. PM2.5 concentrations exceeded the annual mean (40µgm-3) recommended by the National Ambient Air Quality Standards (NAAQS) at Mesra and Bhopal. WSIIs existed in PM2.5 mass at Mesra (50.5%), Bhopal (39.6%), and Mysuru (29.2%). SO42-, NO3-, and NH4+ (SNA) were major secondary inorganic ions in total WSIIs, with an annual average of 88.4% in Mesra and 82.0% in Bhopal 78.4% in Mysuru. Low NO3-/SO42- ratios annually at Mesra (0.41), Bhopal (0.44), and Mysuru (0.24) indicated that stationary sources dominated vehicular emissions (1.0). Aerosol acidity varied from region to region and season to season depending on the presence of NH4+, the dominant counter-ion to neutralize anions. Aerosols were near-neutral or alkaline at all three sites, except during the pre-monsoon season in Mysuru. An assessment of neutralization pathways for major anions [SO42- + NO3-] suggests that they mainly existed as sulfate and nitrate salts such as ammonium sulfate ((NH4)2SO4) and ammonium bisulfate (NH4HSO4) in conjunction with ammonium nitrate (NH4NO3).

Air pollution and child health impacts of decarbonization in 16 global cities: Modelling study

Most research on the air pollution-related health effects of decarbonization has focused on adults. We assess the potential health benefits that could be achieved in children and young people in a global sample of 16 cities through global decarbonization actions. We modelled annual average concentrations of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) at 1x1 km resolution in the cities using a general circulation/atmospheric chemistry model assuming removal of all global combustion-related emissions from land transport, industries, domestic energy use and power generation. We modelled the impact on childhood asthma incidence and adverse birth outcomes (low birthweight, pre-term births) using published exposure–response relationships. Removal of combustion emissions was estimated to decrease annual average PM2.5 by between 2.9 μg/m3 (8.4%) in Freetown and 45.4 μg/m3 (63.7%) in Dhaka. For NO2, the range was from 0.3 ppb (7.9%) in Freetown to 18.8 ppb (92.3%) in Mexico City. Estimated reductions in asthma incidence ranged from close to zero in Freetown, Tamale and Harare to 149 cases per 100,000 population in Los Angeles. For pre-term birth, modelled impacts ranged from a reduction of 135 per 100,000 births in Dar es Salaam to 2,818 per 100,000 births in Bhubaneswar and, for low birthweight, from 75 per 100,000 births in Dar es Salaam to 2,951 per 100,000 births in Dhaka. The large variations chiefly reflect differences in the magnitudes of air pollution reductions and estimated underlying disease rates. Across the 16 cities, the reduction in childhood asthma incidence represents more than one-fifth of the current burden, and an almost 10% reduction in pre-term and low birthweight births. Decarbonization actions that remove combustion-related emissions contributing to ambient PM2.5 and NO2 would likely lead to substantial but geographically-varied reductions in childhood asthma and adverse birth outcomes, though there are uncertainties in causality and the precision of estimates.

Spatiotemporal characteristics and socioeconomic factors of PM2.5 heterogeneity in mainland China during the COVID-19 epidemic

Spatiotemporal variation of PM2.5 in 2018 and 2020 were compared to analyze the impacts of COVID-19, the spatial heterogeneity of PM2.5, and meteorological and socioeconomic impacts of PM2.5 concentrations heterogeneity in China in 2020 were investigated. The results showed that the annual average PM2.5 concentration in 2020 was 32.73 μg/m3 existing a U-shaped variation pattern, which has decreased by 6.38 μg/m3 compared to 2018. A consistent temporal pattern was found in 2018 and 2020 with significant high values in winter and low in summer. PM2.5 declined dramatically in eastern and central China, where are densely populated and economically developed areas during the COVID-19 epidemic compared with previous years, indicating that the significantly decline of social activities had an important effect on the reduction of PM2.5 concentrations. The lowest PM2.5 was found in August because that precipitation had a certain dilution effect on pollutants. January was the most polluted due to centralized coal burning for heating in North China. Overall, the PM2.5 concentrations in China were spatially agglomerated. The highly polluted contiguous zones were mainly located in northwest China and the central plains city group, while the coastal area and Inner Mongolia were areas with good air quality. Negative correlations were found between natural factors (temperature, precipitation, wind speed and relative humidity) and PM2.5 concentrations, with precipitation has the greatest impact on PM2.5, which are beneficial for reducing PM2.5 concentrations. Among the socio-economic factors, proportion of the secondary industry, number of taxis, per capita GDP, population, and industrial nitrogen oxide emissions have positive correlation effects on PM2.5, while the overall social electricity consumption, industrial sulfur dioxide emissions, green coverage in built-up areas, and total gas and liquefied gas supply have negative correlation effects on the PM2.5.

The interaction effects between exposure to ambient PM2.5 and economic development on the settlement intention for floating population in China.

Limited studies examined the interaction effects between exposure to ambient PM2.5 and economic development on the settlement intention of floating population. We used binary logistic model to examine the association of PM2.5, per capita GDP (PGDP), PM2.5 [Formula: see text] PGDP on the settlement intention. Additive interaction term of PM2.5 and PGDP level was used to investigate their interactive effects. Overall, each one grade increment in annual average PM2.5 was associated with decreased probability of settlement intention (OR = 0.847, 95%CI: 0.811-0.885). The interaction effect between PM2.5 and PGDP on settlement intention was significant (OR = 1.168, 95%CI: 1.142-1.194). The stratified analysis showed PM2.5 exhibits lower settlement intention in the aged 55years or above, engaged in low-skilled works, and living in the western China. This study indicates that PM2.5-exposed will decrease the settlement intention of floating population. High economic development level can weaken the relationship between PM2.5 and settlement intention. Policymakers should balance the socio-economic development and environmental health and focus on vulnerable population.

An investigation of PM2.5 concentration changes in Mid-Eastern China before and after COVID-19 outbreak

With the Chinese government revising ambient air quality standards and strengthening the monitoring and management of pollutants such as PM2.5, the concentrations of air pollutants in China have gradually decreased in recent years. Meanwhile, the strong control measures taken by the Chinese government in the face of COVID-19 in 2020 have an extremely profound impact on the reduction of pollutants in China. Therefore, investigations of pollutant concentration changes in China before and after COVID-19 outbreak are very necessary and concerning, but the number of monitoring stations is very limited, making it difficult to conduct a high spatial density investigation. In this study, we construct a modern deep learning model based on multi-source data, which includes remotely sensed AOD data products, other reanalysis element data, and ground monitoring station data. Combining satellite remote sensing techniques, we finally realize a high spital density PM2.5 concentration change investigation method, and analyze the seasonal and annual, the spatial and temporal characteristics of PM2.5 concentrations in Mid-Eastern China from 2016 to 2021 and the impact of epidemic closure and control measures on regional and provincial PM2.5 concentrations. We find that PM2.5 concentrations in Mid-Eastern China during these years is mainly characterized by “north-south superiority and central inferiority”, seasonal differences are evident, with the highest in winter, the second highest in autumn and the lowest in summer, and a gradual decrease in overall concentration during the year. According to our experimental results, the annual average PM2.5 concentration decreases by 3.07 % in 2020, and decreases by 24.53 % during the shutdown period, which is probably caused by China's epidemic control measures. At the same time, some provinces with a large share of secondary industry see PM2.5 concentrations drop by more than 30 %. By 2021, PM2.5 concentrations rebound slightly, rising by 10 % in most provinces.

Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters

This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps.

Association between exposure to ambient PM2.5 and the health status in the mobile population from 338 cities in China.

There are limited studies investigating the relationship between exposure to PM2.5 and the health status among the mobile population. A cross-sectional analysis was performed in a nationally representative sample (2017 China Migrants Dynamic Survey data) consisting of 169,469 mobile population. The ordered logistic regression model was used to examine the association between PM2.5 and the health status in mobile population. Stratified analyses were performed to identify whether the association varied across gender, age group, and regions in China. Overall, every 10μg/m3 increment in annual average PM2.5 was associated with increased risk of poor self-reported health (OR = 1.021, 95% CI: 1.012-1.030). Mobile population aged 31-49years and living in the central region suffers the highest PM2.5-associated health risk (OR = 1.030, 95% CI: 1.019-1.042; OR = 1.095, 95% CI: 1.075-1.116). Our study suggests that PM2.5 exposure was associated with an increased risk of poor self-reported health in mobile population, particularly among the population aged 31-49years and people living in the central region of China. Policymakers should pay more attention to the vulnerable mobile population to tackle the health burden of ambient air pollution.

Long-Term Exposure to Fine Particulate Matter and Incidence of Esophageal Cancer: A Prospective Study of 0.5 Million Chinese Adults

Evidence is sparse and inconclusive on the association between long-term fine (≤2.5 μm) particulate matter (PM2.5) exposure and esophageal cancer. We aimed to assess the association of PM2.5 with esophageal cancer risk and compared the esophageal cancer risk attributable to PM2.5 exposure and other established risk factors. This study included 510,125 participants without esophageal cancer at baseline from China Kadoorie Biobank. A high-resolution (1× 1 km) satellite-based model was used to estimate PM2.5 exposure during the study period. Hazard ratios (HR) and 95% CIs of PM2.5 with esophageal cancer incidence were estimated using Cox proportional hazard model. Population attributable fractions for PM2.5 and other established risk factors were estimated. There was a linear concentration-response relationship between long-term PM2.5 exposure and esophageal cancer. For each 10-μg/m3 increase in PM2.5, the HR was 1.16 (95% CI, 1.04-1.30) for esophageal cancer incidence. Compared with the first quarter of PM2.5 exposure, participants in the highest quarter had a 1.32-fold higher risk for esophageal cancer, with an HR of 1.32 (95% CI, 1.01-1.72). The population attributable risk because of annual average PM2.5 concentration ≥35 μg/m3 was 23.3% (95% CI, 6.6%-40.0%), higher than the risks attributable to lifestyle risk factors. This large prospective cohort study of Chinese adults found that long-term exposure to PM2.5 was associated with an elevated risk of esophageal cancer. With stringent air pollution mitigation measures in China, a large reduction in the esophageal cancer disease burden can be expected.