Motor transport related harmful PM2.5 and PM10: from onroad measurements to the modelling of air pollution by neural network approach on street and urban level

Short-term influence of environmental factors and social variables COVID-19 disease in Spain during first wave (Feb-May 2020)

This study aims to identify the combined role of environmental pollutants and atmospheric variables at short term on the rate of incidence (TIC) and on the hospital admission rate (TIHC) due to COVID-19 disease in Spain. This study used information from 41 of the 52 provinces of Spain (from Feb. 1, 2021 to May 31, 2021). Using TIC and TIHC as dependent variables, and average daily concentrations of PM₁₀ and NO₂ as independent variables. Meteorological variables included maximum daily temperature (Tmax) and average daily absolute humidity (HA). Generalized linear models (GLM) with Poisson link were carried out for each provinces The GLM model controlled for trend, seasonalities, and the autoregressive character of the series. Days with lags were established. The relative risk (RR) was calculated by increases of 10 μg/m³ in PM₁₀ and NO₂ and by 1 °C in the case of Tmax and 1 g/m³ in the case of HA. Later, a linear regression was carried out that included the social determinants of health. Statistically significant associations were found between PM₁₀, NO₂, and the rate of COVID-19 incidence. NO₂ was the variable that showed greater association, both for TIC as well as for TIHC in the majority of provinces. Temperature and HA do not seem to have played an important role. The geographic distribution of RR in the studied provinces was very much heterogeneous. Some of the health determinants considered, including income per capita, presence of airports, average number of diesel cars per inhabitant, average number of nursing personnel, and homes under 30 m² could explain the differential geographic behavior. As findings indicates, environmental factors only could modulate the incidence and severity of COVID-19. Moreover, the social determinants and public health measures could explain some patterns of geographically distribution founded.

Effect of Anti-COVID-19 Measures on Atmospheric Pollutants Correlated with the Economies of Medium-sized Cities in 10 Urban Areas of Grand Est Region, France

Using Sentinel-5P data, this study investigated the magnitude of change in the concentration of air pollutants (NO₂, HCHO, SO₂, O₃, CO, and aerosol index) in the air of ten cities and urban areas of the French region of Grand Est as a result of the first lockdown imposed between March 17, 2020 and May 11, 2020. The results showed that the air quality in the urban environments of Grand Est improved significantly compared to the same period in 2019 without lockdown. NO₂, O₃, aerosol index and CO were the pollutants that exhibited maximum reductions by an average of -33.98%, -5.94%, -26.82% and -0.66%, respectively (the observed maximum decreases were -54.7%, -7.7%, -13.1%, and -5.3%, respectively). The largest decrease occurred in the Public Establishments of Inter-municipal Cooperation (EPCI, in French: Établissement public de coopération intercommunale) areas of Eurométropole de Strasbourg, CA Colmar, and CA Mulhouse Alsace. The maximum decrease in air pollution first occurred in land cover classes close to cities, followed by built-up urban areas. In this study, a global depollution index known as the atmospheric clearance index (ACI) was developed, which involved several air pollution parameters, and quantitatively analyzed the decrease in contamination levels of the atmosphere in this region. In addition, the correlation between the novel ACI and other population and economic development indices was studied. The results indicated that there was a negative and statistically significant correlation between ACI and population density, gross domestic product, gross value added (GVA) at basic prices, number of employees, and active enterprises.

COVID-19 and Air Pollution: Measuring Pandemic Impact to Air Quality in Five European Countries

The rapid spread of the coronavirus (COVID-19) pandemic affected the economy, trade, transport, health care, social services, and other sectors. To control the rapid dispersion of the virus, most countries imposed national lockdowns and social distancing policies. This led to reduced industrial, commercial, and human activities, followed by lower air pollution emissions, which caused air quality improvement. Air pollution monitoring data from the European Environment Agency (EEA) datasets were used to investigate how lockdown policies affected air quality changes in the period before and during the COVID-19 lockdown, comparing to the same periods in 2018 and 2019, along with an assessment of the Index of Production variation impact to air pollution changes during the pandemic in 2020. Analysis results show that industrial and mobility activities were lower in the period of the lockdown along with the reduced selected pollutant NO2, PM2.5, PM10 emissions by approximately 20–40% in 2020.

Trace element concentrations in ambient air as a function of distance from road

Traffic-related air pollution is associated with various adverse health effects. In the absence of more complicated exposure assessment techniques, many environmental health studies have used the natural logarithm of distance to road as a proxy for traffic-related exposures. However, research validating this proxy and further explaining the spatial patterns and elemental composition of traffic-related particulate matter air pollution remains limited. In this study, we collected air samples using a mobile particle concentrator that allowed for high sample loading from major roadways in the Greater Boston Area. We found that concentrations of Cl, Ti, V, Cr, Mn, Fe, Co, Cu, Zn, Sr, Zr, Sn, Ba, and Pb were significantly associated with the natural logarithm of distance to road in coarse particulate matter, and total fine particulate mass concentrations of Al, Ca, Ti, Cr, Mn, Fe, Cu, and Zn were significantly associated with natural logarithm of distance to road in fine particulate matter. Road type (A1 or A2 [primary roads or highways] versus A3 [secondary and connecting roads]) was not a significant predictor of any traffic-related elements in particulate matter air pollution. Our results help identify traffic-related elements in particulate matter air pollution and support the use of logarithm of distance to road as a proxy for traffic-related particulate matter air pollution exposure assessment in epidemiological studies.

Mapping the susceptibility of UNESCO World Cultural Heritage sites in Europe to ambient (outdoor) air pollution

Air pollution, particularly in urban areas, is a concern for its negative effects on the materials of the built environment. Cities are also home to a large part of our cultural heritage. Air pollution accelerates the natural processes of deterioration of the materials of historic buildings and monuments, causing premature aging and reducing their aesthetic value. The present paper aims to assess the current potential damage due to air pollution on different materials through Europe. Several corrosion and soiling maps were produced by applying widely used dose-response functions. One of the priorities of this study was to provide an estimate of the effects of air pollution on UNESCO World Heritage cultural sites throughout Europe. The potential risk for cultural heritage monuments was estimated on the basis of exceeding tolerable degradation thresholds suggested for each material. The results show that, despite the significant improvements in air quality in Europe over the past few decades, air pollution is still considerable and continues to be an important agent of degradation of cultural heritage, particularly in anthropized areas. Although the methodology used in this study provides a simplified assessment of the likelihood of damage to UNESCO's cultural heritage in Europe from air pollution, it provides a unique perspective and the potential risk is assessed on a common basis. The results obtained contribute to a better understanding of the existing risk deriving from atmospheric pollution and to highlighting those sites, generally located in areas where anthropogenic activity is relevant, that need particular attention. The present paper can serve as a basis for stimulating additional studies and site-specific analyzes, as well as highlighting the need for further measures and policies for atmospheric pollution reduction in cities and in the surroundings of sensitive historic buildings and monuments to prevent further damage.

The relationship between air pollution and COVID-19-related deaths: An application to three French cities

Being heavily dependent to oil products (mainly gasoline and diesel), the French transport sector is the main emitter of Particulate Matter (PMs) whose critical levels induce harmful health effects for urban inhabitants. We selected three major French cities (Paris, Lyon, and Marseille) to investigate the relationship between the Coronavirus Disease 19 (COVID-19) outbreak and air pollution. Using Artificial Neural Networks (ANNs) experiments, we have determined the concentration of PM_2.5 and PM₁₀ linked to COVID-19-related deaths. Our focus is on the potential effects of Particulate Matter (PM) in spreading the epidemic. The underlying hypothesis is that a pre-determined particulate concentration can foster COVID-19 and make the respiratory system more susceptible to this infection. The empirical strategy used an innovative Machine Learning (ML) methodology. In particular, through the so-called cutting technique in ANNs, we found new threshold levels of PM_2.5 and PM₁₀ connected to COVID-19: 17.4 µg/m³ (PM_2.5) and 29.6 µg/m³ (PM₁₀) for Paris; 15.6 µg/m³ (PM_2.5) and 20.6 µg/m³ (PM₁₀) for Lyon; 14.3 µg/m³ (PM_2.5) and 22.04 µg/m³ (PM₁₀) for Marseille. Interestingly, all the threshold values identified by the ANNs are higher than the limits imposed by the European Parliament. Finally, a Causal Direction from Dependency (D2C) algorithm is applied to check the consistency of our findings.

Factors affecting ambient endotoxin and particulate matter concentrations around air vents of subway stations in South Korea

Levels of airborne endotoxins and particulate matter less than 10 μm and 2.5 μm in diameter (PM) were measured in the air vents of subway stations in Seoul, South Korea, and factors affecting both pollutants were analyzed. The measurements were completed from March 2016 to February 2017 for eight air vents situated at the ground level around the subway stations. A total of 166 air samples were collected and analyzed using the kinetic limulus amebocyte lysate assay. Endotoxin levels ranged from not detected to 1.986 EU m^-3, with a mean of 0.227 EU m^-3. The results showed significantly different PM levels from the measurements reported by AIRKOREA as part of the comprehensive air quality index. This can be attributed to different sampling sites in the same area. Endotoxin levels tended to be higher in fall compared to summer. Airborne bacteria levels showed a pattern similar to the endotoxin levels, but no significant association was reported between them. The levels of endotoxins around air vents with a glass cover and streets that allowed smoking were significantly higher than those not containing a walled barrier and streets in which smoking was prohibited. Multivariate regression analysis showed that the factors affecting endotoxin levels comprised air vents with a glass cover (coefficient = 0.106, p = 0.014) and season (coefficient = 0.062, p < 0.0001). Therefore, installing barriers on the air vents and prohibiting smoking in streets to which the vents open may be effective ways to lessen exposure to airborne endotoxin levels around air vents.