An Assessment of the On-Road Mobile Sources Contribution to Particulate Matter Air Pollution by AERMOD Dispersion Model

On-road mobile sources of emissions make important contributions to particulate matter pollution (PM2.5–PM10) in cities. The quantification of such pollution is, however, highly challenging due to the number of interacting factors that affect emissions such as vehicle category, emission standard, vehicle speed and weather conditions. The proper identification of individual sources of emission is particularly necessary for air quality management areas. In this study, we estimated exhaust and non-exhaust traffic-related PM2.5 and PM10 contributions to total ambient pollution in Banská Bystrica (Slovak republic) by simulation based on the AERMOD dispersion model. Emission rates of particular vehicle categories were obtained through vehicle population statistics, traffic data survey and emission factors from the EMEP/EEA air pollutant emission inventory guidebook. Continuous PM10 and PM2.5 data from air quality monitoring stations were analysed for the years 2019–2020 and compared with modelled concentrations. The annual concentration values of PM2.5 and PM10 in the study area reached 16.71 μg/m3 and 15.57 μg/m3, respectively. We found that modelled PM2.5 peak concentration values exceeded the WHO air quality guideline annual mean limit. Traffic-related PM2.5 and PM10 contributions to ambient pollution at the reference point located nearby to a busy traffic route were approximately 25% and 17%, respectively. The reference point located outside the main transport corridors showed an approximately 11% contribution, both for PM2.5 and PM10 concentrations. The simulations showed that PM pollution is greatly contributed to by on-road mobile sources of emissions in the study area, and especially non-exhaust emissions, which require serious attention in association with their health impacts and the selection of Banská Bystrica as an air quality management area.

Ambiguous Air Pollution Effects of China’s COVID-19 Lock-down

Reductions in ambient pollution have been suggested as a "silver lining" to the COVID-19 pandemic. We analyze China's pollution monitor data and account for the large annual improvements in air quality following the Lunar New Year, which essentially coincided with lock-downs. With the exception of nitrogen dioxide, China's air quality improvements in 2020 are smaller than we should expect near the pandemic's epicenter, Hubei province. We see smaller improvements in sulfur dioxide than expected, while ozone concentrations roughly doubled in Hubei. Similar patterns are found for the six provinces neighboring Hubei. We conclude that COVID-19 had ambiguous impacts on China's air quality.

Assessment of agricultural ecosystem benzo(a)pyrene pollution on a wheat field model

The represented research aims to assess the pollution of agricultural ecosystems with benzo(a)pyrene. The study considers the exhaust gases of heavy farming machinery as a primary source of pollution. This dangerous carcinogen entering air with internal combustion engines exhausts goes to the water and soil, and then, through the trophic chains, in a human body. Alongside the obvious negative impacts on human health, the benzo(a)pyrene exposure manifests through the decline in the agricultural ecosystems’ productivity, soil fertility, etc. The assessment was implemented on a wheat field model during harvest using the direct combining method. The model doesn’t consider the ambient pollution sources. The performed simulations allow disclosing the correlations between the harvesting machine’s velocity and pollutant’s emission mass on the model field, identifying the most environmentally dangerous engine’s operation mode, and pointing out the possible ways to decrease the benzo(a)pyrene impacts on agricultural ecosystems.

Possible synergistic effect of air pollution on increasing severity of SARS-CoV-2

SARS-CoV-2 infection started in the last days of 2019 in China and affected a great number of people worldwide, causing many deaths in numerous countries. Major clinical symptoms of the infected patients are found to be fever, cough, and shortening of breath leading to acute respiratory distress syndrome (ARDS). Cytokine storm and inflammatory responses have been introduced as the main causes of respiratory distress in severe cases. Moreover, all these inflammatory factors have been systematically expressed in the human body through chronic exposure to ambient pollution due to an industrial lifestyle and lead to respiratory problems. In order to assess the possible synergistic effect of air pollution on the increased severity of COVID-19, the number of days and the value of air quality index (AQI) as well as the amount of four ambient pollutants (PM2.5, PM10, O3, and NO2) with unhealthy ranges were measured for three years in eight cities of Iran with different numbers of hospitalized patients affected with SARS-CoV-2. The correlation coefficient between the number of hospitalized patients and air pollution factors was calculated. The present data revealed a significant positive correlation between unhealthy ranges of O3 and NO2 and the number of hospitalized patients with COVID-19. No correlation was found between PM2.5, PM10, and AQIs and the increased number of severe cases. Conclusively, these primary results might show the synergistic effect of chronic exposure to air pollutants due to living in polluted areas and the increased severity of COVID-19 disease.

The impacts of commissioning coal-fired power stations on air quality in South Africa: insights from ambient monitoring stations

The South African electricity sector is known for its heavy reliance on coal. The aim of this study is to assess the impacts of increasing SO2 and PM emissions from the three return-to-service power stations (Komati, Camden and Grootvlei), and the newly constructed Medupi power station on ambient air quality measured in the vicinities of these power stations. Trends in ambient pollution concentrations were determined using Theil-Sen analysis. The correlation between the emissions and ambient pollution concentrations at nearby monitoring stations was determined with the Spearman partial rank correlation coefficient. Lastly, compliance of ambient pollution concentrations with the South Africa National Ambient Air Quality Standards was assessed. Few statistically significant trends in ambient SO2 and PM10 concentrations are found, and there is little correlation between increasing power station emissions and ambient pollutant concentrations in the vicinity. It is only at Camden monitoring station where there are increases in PM10 concentrations from the direction of Camden power station, and at Grootvlei monitoring station where increasing SO2 concentrations are from the directions of Grootvlei and Lethabo power stations. A strong, positive correlation between power station emissions and ambient concentrations exists only for SO2 at Grootvlei monitoring station and PM10 at Medupi monitoring station (although it is likely that the correlation at Medupi is related to construction and vehicle activity, and not emissions from Medupi power station stacks). It is concluded that the establishment of monitoring stations in the vicinities of power stations is necessary but not sufficient to monitor their impact on air quality in the surrounds.