Air pollution and inhalation exposure to particulate matter of different sizes in rural households using improved stoves in central China

Due to the suspension of traffic mobility and industrial activities during the COVID-19, particulate matter (PM) pollution has decreased in China. However, rarely have research studies discussed the spatiotemporal pattern of this change and related influencing factors at city-scale across the nation. In this research, the clustering patterns of the decline rates of PM2.5 and PM10 during the period from 20 January to 8 April in 2020, compared with the same period of 2019, were investigated using spatial autocorrelation analysis. Four meteorological factors and two socioeconomic factors, i.e., the decline of intra-city mobility intensity (dIMI) representing the effect of traffic mobility and the decline rates of the secondary industrial output values (drSIOV), were adopted in the regression analysis. Then, multi-scale geographically weighted regression (MGWR), a model allowing the particular processing scale for each independent variable, was applied for investigating the relationship between PM pollution reductions and influencing factors. For comparison, ordinary least square (OLS) regression and the classic geographically weighted regression (GWR) were also performed. The research found that there were 16% and 20% reduction of PM2.5 and PM10 concentration across China and significant PM pollution mitigation in central, east, and south regions of China. As for the regression analysis results, MGWR outperformed the other two models, with R2 of 0.711 and 0.732 for PM2.5 and PM10, respectively. The results of MGWR revealed that the two socioeconomic factors had more significant impacts than meteorological factors. It showed that the reduction of traffic mobility caused more relative declines of PM2.5 in east China (e.g., cities in Jiangsu), while it caused more relative declines of PM10 in central China (e.g., cities in Henan). The reduction of industrial operation had a strong relationship with the PM10 drop in northeast China. The results are crucial for understanding how the decline pattern of PM pollution varied spatially during the COVID-19 outbreak, and it also provides a good reference for air pollution control in the future.

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Air Pollution Particulate Matter Alters Antimycobacterial Respiratory Epithelium Innate Immunity

Infection and Immunity ◽

10.1128/iai.03018-14 ◽

2015 ◽

Vol 83 (6) ◽

pp. 2507-2517 ◽

Cited By ~ 49

Author(s):

César E. Rivas-Santiago ◽

Srijata Sarkar ◽

Pasquale Cantarella ◽

Álvaro Osornio-Vargas ◽

Raúl Quintana-Belmares ◽

...

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Epithelial Cells ◽

Immune Responses ◽

Inhalation Exposure ◽

A549 Cells ◽

Ambient Air ◽

Ambient Air Pollution ◽

Alveolar Type ◽

Content Type

Inhalation exposure to indoor air pollutants and cigarette smoke increases the risk of developing tuberculosis (TB). Whether exposure to ambient air pollution particulate matter (PM) alters protective human host immune responses againstMycobacterium tuberculosishas been little studied. Here, we examined the effect of PM from Iztapalapa, a municipality of Mexico City, with aerodynamic diameters below 2.5 μm (PM2.5) and 10 μm (PM10) on innate antimycobacterial immune responses in human alveolar type II epithelial cells of the A549 cell line. Exposure to PM2.5or PM10deregulated the ability of the A549 cells to express the antimicrobial peptides human β-defensin 2 (HBD-2) and HBD-3 upon infection withM. tuberculosisand increased intracellularM. tuberculosisgrowth (as measured by CFU count). The observed modulation of antibacterial responsiveness by PM exposure was associated with the induction of senescence in PM-exposed A549 cells and was unrelated to PM-mediated loss of cell viability. Thus, the induction of senescence and downregulation of HBD-2 and HBD-3 expression in respiratory PM-exposed epithelial cells leading to enhancedM. tuberculosisgrowth represent mechanisms by which exposure to air pollution PM may increase the risk ofM. tuberculosisinfection and the development of TB.

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AMBIENT PARTICULATE MATTER (PM10 AND PM2.5) AIR POLLUTION IN DESNIANSKY DISTRICT IN KYIV

Hygiene of populated places ◽

10.32402/hygiene2017.67.031 ◽

2017 ◽

Vol 2017 (67) ◽

pp. 31-37

Author(s):

O. Turos ◽

◽

T. Maremukha ◽

I. Kobzarenko ◽

A. Petrosian ◽

...

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Ambient Particulate Matter ◽

Pm10 And Pm2.5

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Vicious Cycle of Economic Growth, Pm2.5 and COVID-19 Case of G7 Countries

10.31124/advance.12937019.v1 ◽

2020 ◽

Author(s):

Rıdvan Karacan

Keyword(s):

Economic Growth ◽

Air Pollution ◽

Particulate Matter ◽

Fossil Fuels ◽

Fine Particulate Matter ◽

Panel Cointegration ◽

Air Pollutant ◽

Population Exposure ◽

Causality Analysis ◽

G7 Countries

Today, production is carried out depending on fossil fuels. Fossil fuels pollute the air as they contain high levels of carbon. Many studies have been carried out on the economic costs of air pollution. However, in the present study, unlike the former ones, economic growth's relationship with the COVID-19 virus in addition to air pollution was examined. The COVID-19 virus, which was initially reported in Wuhan, China in December 2019 and affected the whole world, has caused many cases and deaths. Researchers have been going on studying how the virus is transmitted. Some of these studies suggest that the number of virus-related cases increases in regions with a high level of air pollution. Based on this fact, it is thought that air pollution will increase the number of COVID-19 cases in G7 Countries where industrial production is widespread. Therefore, the negative aspects of economic growth, which currently depends on fossil fuels, is tried to be revealed. The research was carried out for the period between 2000-2019. Panel cointegration test and panel causality analysis were used for the empirical analysis. Particulate matter known as PM2.5[1] was used as an indicator of air pollution. Consequently, a positive long-term relationship has been identified between PM2.5 and economic growth. This relationship also affects the number of COVID-19 cases. [1] "Fine particulate matter (PM2.5) is an air pollutant that poses the greatest risk to health globally, affecting more people than any other pollutant (WHO, 2018). Chronic exposure to PM2.5 considerably increases the risk of respiratory and cardiovascular diseases in particular (WHO, 2018). For these reasons, population exposure to (outdoor or ambient) PM2.5 has been identified as an OECD Green Growth headline indicator" (OECD.Stat).

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When Particulate Matter Strikes Cities. Social Disparities and Health Costs of Air Pollution

SSRN Electronic Journal ◽

10.2139/ssrn.3430573 ◽

2019 ◽

Author(s):

Matilde Giaccherini ◽

Joanna Kopinska ◽

Alessandro Palma

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Health Costs ◽

Social Disparities

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Pilot Operation of Vehicle Rating System in Seoul’s Green Transportation Zone and the Reduction of Particulate Matter and Air Pollution Levels

The Korean Association of Urban Policies ◽

10.21447/jup.2020.11.2.79 ◽

2020 ◽

Vol 11 (2) ◽

pp. 79-94

Author(s):

Sangjin Park ◽

Hyungkyoo Kim

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Rating System ◽

Pollution Levels ◽

Green Transportation

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Emerging Challenges of Air Pollution and Particulate Matter in China, India, and Pakistan and Mitigating Solutions

Journal of Hazardous Materials ◽

10.1016/j.jhazmat.2021.125851 ◽

2021 ◽

pp. 125851

Author(s):

Muhammad Naveed Anwar ◽

Muneeba Shabbir ◽

Eza Tahir ◽

Mahnoor Iftikhar ◽

Hira Saif ◽

...

Keyword(s):

Air Pollution ◽

Particulate Matter

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A global association between Covid-19 cases and airborne particulate matter at regional level

Scientific Reports ◽

10.1038/s41598-021-85751-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Angelo Solimini ◽

F. Filipponi ◽

D. Alunni Fegatelli ◽

B. Caputo ◽

C. M. De Marco ◽

...

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Negative Binomial ◽

Numerical Models ◽

Regional Level ◽

Pollution Level ◽

Observation Data ◽

Cumulative Number ◽

Mixed Effect

AbstractEvidences of an association between air pollution and Covid-19 infections are mixed and inconclusive. We conducted an ecological analysis at regional scale of long-term exposure to air-borne particle matter and spread of Covid-19 cases during the first wave of epidemics. Global air pollution and climate data were calculated from satellite earth observation data assimilated into numerical models at 10 km resolution. Main outcome was defined as the cumulative number of cases of Covid-19 in the 14 days following the date when > 10 cumulative cases were reported. Negative binomial mixed effect models were applied to estimate the associations between the outcome and long-term exposure to air pollution at the regional level (PM10, PM2.5), after adjusting for relevant regional and country level covariates and spatial correlation. In total we collected 237,749 Covid-19 cases from 730 regions, 63 countries and 5 continents at May 30, 2020. A 10 μg/m3 increase of pollution level was associated with 8.1% (95% CI 5.4%, 10.5%) and 11.5% (95% CI 7.8%, 14.9%) increases in the number of cases in a 14 days window, for PM2.5 and PM10 respectively. We found an association between Covid-19 cases and air pollution suggestive of a possible causal link among particulate matter levels and incidence of COVID-19.

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