Association between air pollution and COVID-19 mortality and morbidity

Coronavirus disease (COVID-19) pandemic is affecting the world unevenly. One of the highest numbers of cases were recorded in the most polluted regions worldwide. The risk factors for severe COVID-19 include diabetes, cardiovascular, and respiratory diseases. It has been known that the same disease might be worsened by chronic exposure to air pollution. The study aimed to determine whether long-term average exposure to air pollution is associated with an increased risk of COVID-19 cases and deaths in Poland. The cumulative number of COVID-19 cases and deaths for each voivodeship (the main administrative level of jurisdictions) in Poland were collected from March 4, 2020, to May 15, 2020. Based on the official data published by Chief Inspectorate of Environmental Protection voivodeship-level long-term exposure to main air pollution: PM2.5, PM10, NO2, SO2, O3 (averaged from 2013 to 2018) was established. There were statistically significant correlation between COVID-19 cases (per 100,000 population) and annual average concentration of PM2.5 (R2 = 0.367, p = 0.016), PM10 (R2 = 0.415, p = 0.009), SO2 (R2 = 0.489, p = 0.003), and O3 (R2 = 0.537, p = 0.0018). Moreover, COVID-19 deaths (per 100,000 population) were associated with annual average concentration of PM2.5 (R2 = 0.290, p = 0.038), NO2 (R2 = 0.319, p = 0.028), O3 (R2 = 0.452, p = 0.006). The long-term exposure to air pollution, especially PM2.5, PM10, SO2, NO2, O3 seems to play an essential role in COVID-19 prevalence and mortality. Long-term exposure to air pollution might increase the susceptibility to the infection, exacerbates the severity of SARS-CoV-2 infections, and worsens the patients’ prognosis. The study provides generalized and possible universal trends. Detailed analyzes of the phenomenon dedicated to a given region require taking into account data on comorbidities and socioeconomic variables as well as information about the long-term exposure to air pollution and COVID-19 cases and deaths at smaller administrative level of jurisdictions (community or at least district level).

Impact of COVID-19 on Extremely Polluted Air Quality and Trend Forecast in Seven Provinces and Three Cities of China

At the beginning of 2020, COVID-19 broke out. Because the virus is extremely contagious and the mortality rate after infection is extremely high, China and many countries in the world have imposed lockdowns. Air pollutants during the epidemic period have attracted the attention of many scholars. This research is to use predictive models to describe changes in extreme air pollutants. China is the first country in the world to enter the lockdown state. This study uses data from 2015-2020 to compare and predict the concentration of extreme pollutants before and after the lockdown. The results show that the lockdown of the epidemic will reduce the annual average concentration of PM2.5, and the annual average concentration of O3 will increase first and then decrease. Through analysis, it is concluded that there is a synergistic decrease trend between PM2.5 and O3. With the various blockade measures for epidemic prevention and control, the reduction of extreme air pollutant concentrations is sustainable. The assessment of China’s air quality in conjunction with the COVID-19 can provide scientific guidance for the Chinese government and other relevant departments to formulate policies.

The Temporal And Spatial Changes Of Beijing’s Pm 2.5 Concentration And Its Relationship With Meteorological Factors From 2015 To 2020

Currently, Beijing is facing increasing serious air quality problems. Atmospheric pollutants in Beijing are mainly composed of particulate matter, which is a key factor leading to adverse effects on human health. This paper uses hourly data from 36 environmental monitoring stations in Beijing from 2015 to 2020 to obtain the temporal and spatial distribution of the mass concentration of particulate matter with a diameter smaller than 2.5 μm (PM2.5). The 36 stations established by the Ministry of Ecology and Environment and the Beijing Environmental Protection Monitoring Center and obtain continuous real-time monitoring of particulate matter. And the 36 stations are divided into 13 main urban environmental assessment points, 11 suburban assessment points, 1 control point, 6 district assessment points, and 5 traffic pollution monitoring points. The annual average concentration of PM2.5 in Beijing was 60 μg/m3 with a negative trend of approximately 14% year-1. In urban areas the annual average concentration of PM2.5 was 59 μg/m3, in suburbs 56 μg/m3, in traffic areas 63 μg/m3, and in district areas 62 μg/m3. From 2015 to 2020, in urban areas PM2.5 decreased by 14% year-1, in suburbs by 15% year -1, in traffic areas by 15% year-1, and in district areas by 12% year-1. The quarterly average concentrations of PM2.5 in winter andspring are higher than those in summer and autumn (64 μg/m3, 59 μg/m3, 45 μg/m3, 55 μg/m3, respectively). The influenceof meteorological factors on the daily average value of PM2.5 in each season was analysed. The daily average PM2.5 in spring, summer, autumn and winter is significantly negatively correlated with daily average wind speed, sunshine hours, and air pressure, and significantly positively correlated with daily average rainfall and relative humidity. Except for autumn, the daily average PM2.5 is positively correlated with temperature. Although Beijing’s PM2.5 has been declining since the adoption of the‘Air Pollution Prevention and Control Action Plan’, it is still far from the first level of the new ‘Ambient Air Quality Standard’(GB309S-2012) formulated by China in 2012.

Long-Term Exposure to PM2.5, Facemask Mandates, Stay Home Orders and COVID-19 Incidence in the United States

Long-term PM2.5 exposure might predispose populations to SARS-CoV-2 infection and intervention policies might interrupt SARS-CoV-2 transmission and reduce the risk of COVID-19. We conducted an ecologic study across the United States, using county-level COVID-19 incidence up to 12 September 2020, to represent the first two surges in the U.S., annual average of PM2.5 between 2000 and 2016 and state-level facemask mandates and stay home orders. We fit negative binomial models to assess COVID-19 incidence in association with PM2.5 and policies. Stratified analyses by facemask policy and stay home policy were also performed. Each 1-µg/m3 increase in annual average concentration of PM2.5 exposure was associated with 7.56% (95% CI: 3.76%, 11.49%) increase in COVID-19 risk. Facemask mandates and stay home policies were inversely associated with COVID-19 with adjusted RRs of 0.8466 (95% CI: 0.7598, 0.9432) and 0.9193 (95% CI: 0.8021, 1.0537), respectively. The associations between PM2.5 and COVID-19 were consistent among counties with or without preventive policies. Our study added evidence that long-term PM2.5 exposure increased the risk of COVID-19 during each surge and cumulatively as of 12 September 2020, in the United States. Although both state-level implementation of facemask mandates and stay home orders were effective in preventing the spread of COVID-19, no clear effect modification was observed regarding long-term exposure to PM2.5 on the risk of COVID-19.

Evaluation and Prediction of PM10 and PM2.5 from Road Source Emissions in Kuala Lumpur City Centre

Particulate matter (PM) is one of the major pollutants emitted by vehicles that adversely affect human health and the environment. This study evaluates and predicts concentrations and dispersion patterns of PM10 and PM2.5 in Kuala Lumpur city centre. The OML-Highway model calculates hourly time series of PM10 and PM2.5 concentrations and distribution caused by traffic emissions under different scenarios; business as usual (BAU) and 30% traffic reduction to see the impact of traffic reduction for sustainable traffic management. Continuous PM10 and PM2.5 data from a nearby monitoring station were analysed for the year 2019 and compared with modelled concentrations. Annual average concentration at various locations of interest for PM10 and PM2.5 during BAU runs were in the ranges 41.4–65.9 µg/m3 and 30.4–43.7 µg/m3 respectively, compared to during the 30% traffic reduction run ranging at 40.5–59.5 µg/m3 and 29.9–40.3 µg/m3 respectively. The average concentration of PM10 and PM2.5 at the Continuous Air Quality Monitoring Station (CAQMS) was 36.4 µg/m3 and 28.2 µg/m3 respectively. Strong correlations were observed between the predicted and observed data for PM10 and PM2.5 in both scenarios (p < 0.05). This research demonstrated that the reduction of traffic volume in the city contributes to reducing the concentration of particulate matter pollution.

Factors Influencing the Settlement Intentions of Chinese Migrants in Cities: An Analysis of Air Quality and Higher Income Opportunity as Predictors

With rapid urbanization, the air pollution issue is becoming an increasingly serious issue given that people are strongly swayed in their location choice to settle down in a growing urban area where most job opportunities have been created. This study investigated the influences of both air quality and income on the settlement intentions of Chinese migrants by using microlevel samples of the China Migrants Dynamic Survey (CMDS) data from 2017 and the annual average concentration of PM2.5 (particles with diameter ≤ 2.5 μm in the air) to measure a city’s air quality. The results showed that the settlement decisions of Chinese migrants involved a trade-off between income and air quality. Poorer air quality could significantly decrease the settlement intention, while a higher income could significantly increase the settlement intention of Chinese migrants. However, as the migrants’ income opportunity increased at a location, the negative influence of poorer air quality on the settlement intention at that location gradually declined. Specifically, when deciding whether to settle down in cities, the migrants with a non-agricultural “hukou” (household registration) tended to pay more attention to air quality than the migrants with an agricultural “hukou,” and migrants who moved farther away in geographic distance tended to pay more attention to income. It was concluded that the influences of air quality and income on the settlement intentions of the migrants were robust and consistent after using different estimation methods and considering the issue of endogeneity.

Temporal and Spatial Variation of PM2.5 in Xining, Northeast of the Qinghai–Xizang (Tibet) Plateau

PM2.5 was sampled from January 2017 to May 2018 at an urban, suburban, industrial, and rural sites in Xining. The annual mean of PM2.5 was highest at the urban site and lowest at the rural site, with an average of 51.5 ± 48.9 and 26.4 ± 17.8 μg·m−3, respectively. The average PM2.5 concentration of the industrial and suburban sites was 42.8 ± 27.4 and 37.2 ± 23.7 μg·m−3, respectively. All sites except for the rural had concentrations above the ambient air quality standards of China (GB3095-2012). The highest concentration of PM2.5 at all sites was observed in winter, followed by spring, autumn, and summer. The concentration of major constituents showed statistically significant seasonal and spatial variation. The highest concentrations of organic carbon (OC), elemental carbon (EC), water-soluble organic carbon (WSOC), and water-soluble inorganic ions (WSIIs) were found at the urban site in winter. The average concentration of F− was higher than that in many studies, especially at the industrial site where the annual average concentration of F− was 1.5 ± 1.7 μg·m−3. The range of sulfur oxidation ratio (SOR) was 0.1–0.18 and nitrogen oxidation ratio (NOR) was 0.02–0.1 in Xining. The higher SO42−/NO3− indicates that coal combustion has greater impact than vehicle emissions. The results of the potential source contribution function (PSCF) suggest that air mass from middle- and large-scale transport from the western areas of Xining have contributed to the higher level of PM2.5. On the basis of the positive matrix factorization (PMF) model, it was found that aerosols from salt lakes and dust were the main sources of PM2.5 in Xining, accounting for 26.3% of aerosol total mass. During the sandstorms, the concentration of PM2.5 increased sharply, and the concentrations of Na+, Ca2+ and Mg2+ were 1.13–2.70, 1.68–4.41, and 1.15–5.12 times higher, respectively, than annual average concentration, implying that aerosols were mainly from dust and the largest saltwater lake, Qinghai Lake, and many other salt lakes in the province of Qinghai. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) was utilized to study the surface components of PM2.5 and F− was found to be increasingly distributed from the surface to inside the particles. We determined that the extremely high PM2.5 concentration appears to be due to an episode of heavy pollution resulting from the combination of sandstorms and the burning of fireworks.

Benzo[a]pyrene in the Ambient Air in the Czech Republic: Emission Sources, Current and Long-Term Monitoring Analysis and Human Exposure

This paper provides a detailed, thorough analysis of air pollution by benzo[a]pyrene (BaP) in the Czech Republic. The Czech residential sector is responsible for more than 98.8% of BaP, based on the national emission inventory. According to the data from 48 sites of the National Air Quality Monitoring Network, the range of annual average concentration of BaP ranges from 0.4 ng·m−3 at a rural regional station to 7.7 ng·m−3 at an industrial station. Additionally, short-term campaign measurements in small settlements have recorded high values of daily benzo[a]pyrene concentrations (0.1–13.6 ng·m−3) in winter months linked to local heating of household heating. The transboundary contribution to the annual average concentrations of BaP was estimated by the CAMx model to range from 46% to 70% over most of the country. However, the contribution of Czech sources can exceed 80% in residential heating hot spots. It is likely that the transboundary contribution to BaP concentrations was overestimated by a factor of 1.5 due to limitations of the modeling approach used. During the period of 2012–2018, 35–58% of the urban population in the Czech Republic were exposed to BaP concentrations above target. A significant decreasing trend, estimated by the Mann-Kendall test, was found for annual and winter BaP concentrations between 2008 and 2018.

Investigation of the Relationship Between the Level of Fine Particulate Matter and Stroke Mortality Rate in Mashhad in 2014 and 2015

Background: Air pollution is a major social problem, particularly in developing countries, where the rapid expansion of industries, cities, and traffic is the main cause of increased air pollution. Objectives: This ecological study (correlation) has been conducted with the aim of analyzing the correlation between ambient fine particulate matter (PM2.5) amount and the rate of stroke mortality in Mashhad during the years 2014 and 2015. Methods: Data were collected from hospitals, the Monitoring Center of Environmental Pollutants, and the Bureau of Meteorology in Khorasan Razavi Province and were analyzed to evaluate the correlation. Results: The results show that the correlation coefficient between PM2.5 and the rate of stroke mortality in different seasons in 2014 and 2015 are 0.997 and 0.902, respectively. The correlation was stronger in 2014 and is significant at a confidence level of 0.01. Conclusions: According to the results, the annual average concentration of PM2.5 decreased from 29.261 (μg/m3) in 2014 to 25.283 (μg/m3) in 2015, and also, the annual rate of stroke mortality decreased by 4.4% in 2015.

Numerical Study of the impact of Meteorological and Emission control on the decreasing of PM2.5 concentration in Beijing by WRF-SMOKE-CMAQ model system

&lt;p&gt;In the past years, the PM2.5 concentration in Beijing decreases from 89 ug/m&lt;sup&gt;3&lt;/sup&gt; in 2013 to 42 ug/m&lt;sup&gt;3&lt;/sup&gt; in 2019, especially in the recent three years, that the PM2.5 concentration rapidly decreases from 73 ug/m&lt;sup&gt;3&lt;/sup&gt; in 2016 decreases to 42 ug/m&lt;sup&gt;3&lt;/sup&gt;. An air quality modeling system, based on WRF-SMOKE-CMAQ model, was established before APEC 2014 to forecast daily air quality and assess future air quality improvement plans, which plan expects Beijing&amp;#8217;s PM2.5 would reach to 53 ug/m&lt;sup&gt;3&lt;/sup&gt; in 2020, and reach to 35 ug/m&lt;sup&gt;3&lt;/sup&gt; in 2030. Actually, the PM2.5 concentration in Beijing has fallen faster than expected, that the annual PM2.5 concentration is 42 ug/m&lt;sup&gt;3&lt;/sup&gt; in 2019. So how much influence do meteorological factors and emission control have on the annual PM2.5 concentration? The WRF-SMOKE-CMAQ modeling system has been used to re-build the PM2.5 concentration characteristics of Beijing from 2013 to 2019 to distinguish these two factors. Preliminary results show that under the same emission scenarios, the annual average concentration of PM2.5 in Beijing in 2013 was 68.6 ug/m&lt;sup&gt;3&lt;/sup&gt;, and the average annual concentration of PM2.5 in 2017 was 69.4 ug/m&lt;sup&gt;3&lt;/sup&gt;. More detailed model results will be presented.&lt;/p&gt;