Diagnostic Analysis and Modelling of Air Quality for Medium Sized City

It is witnessed that air pollution is an important issue regarding not only for human health but also for plants, animals and building materials. Increase in industrialisation, abundant use of automobiles, and network of highways, the quality of air of Amravati city is degrading day by day. The data has been collected for a period ranging from March 2020 to February 2021 for analysis and pollution forecasting model work. The concentration of Suspended Particulate Matter (SPM), Respiratory Suspended Particulate Matter (RSPM), Sulphur dioxide (SO2), Nitrogen dioxide (NO2) and Ozone (O3) have been monitored over successive periods of time and also data is collected from monitoring stations controlled by MPCB. Numerous studies have been proposed for predicting pollution concentrations and improvement of performance of predictable models is an important issue. As is well known, collaborative observations proved that it can improve predictive performance. In this study, multivariate linear regression approach-based model was constructed to predict the RSPM in the air using the meteorological (air temperature, relative humidity, wind speed, rainfall) and air quality monitoring data (SPM, NO2, SO2, O3). Correlation between measured and model predicted vales of RSPM were 0.717,0.691,0.64 and 0.60 for winter, summer, monsoon and annual seasons respectively. However, the regression model based on seasonal data for winter was found to be more effective.

Spatial and Temporal Distributions of Air Pollutants in Nanchang, Southeast China during 2017–2020

In response to COVID-19 in December 2019, China imposed a strict lockdown for the following two months, which led to an unprecedented reduction in industrial activities and transportation. However, haze pollution was still recorded in many Chinese cities during the lockdown period. To explore temporal and spatial variations in urban haze pollution, concentrations of air pollutants (PM2.5, PM10, SO2, CO, NO, NO2, and O3) from April 2017 to March 2020 were observed at 23 monitoring stations throughout Nanchang City (including one industrial site, sixteen urban central sites, two mountain sites, and four suburban sites). Overall, the highest concentrations of PM2.5, PM10, and SO2 were observed at industrial sites and the highest CO and NOx (NO and NO2) concentrations were recorded at urban sites. The air pollutants at mountain sites all showed the lowest concentrations, which indicated that anthropogenic activities are largely responsible for air pollutants. Concentrations of PM2.5, PM10, CO, NO, and NO2 showed similar season trends, that is, the highest levels in winter and lowest concentrations in summer, but an opposite season pattern for O3. Except for a sharply dropping pattern from January to May 2018, there were no seasonal patterns for SO2 concentration in all the observed sites. Daily PM2.5, PM10, CO, NOx, and SO2 concentrations showed a peak during the morning commute, which indicated the influences of anthropogenic activities on PM2.5, PM10, CO, NOx, and SO2. PM2.5, PM10, NOx, and CO concentrations at industrial, urban, and suburban sites were higher during nighttime than during daytime, but they showed the opposite pattern at mountain sites. In addition, PM2.5, PM10, CO, and NOx concentrations were lower during the lockdown period (D2) than those before the lockdown (B1). After the lockdown was lifted (A3), PM2.5, PM10, CO, and NOx concentrations showed a slowly increasing trend. However, O3 concentrations continuously increased from B1 to A3.

30 Years of Air Quality Trends in Japan

The aim of this paper is to obtain information that will contribute to measures and research needed to further improve the air quality in Japan. The trends and characteristics of air pollutant concentrations, especially PM2.5, ozone, and related substances, over the past 30 years, are analyzed, and the relationships between concentrations and emissions are discussed quantitatively. We found that PM2.5 mass concentrations have decreased, with the largest reduction in elemental carbon (EC) as the PM2.5 component. The concentrations of organic carbon (OC) have not changed significantly compared to other components, suggesting that especially VOC emissions as precursors need to be reduced. In addition, the analysis of the differences in PM2.5 concentrations between the ambient and the roadside showed that further research on non-exhaust particles is needed. For NOx and SO2, there is a linear relationship between domestic anthropogenic emissions and atmospheric concentrations, indicating that emission control measures are directly effective in the reduction in concentrations. Also, recent air pollution episodes and the effect of reduced economic activity, as a consequence of COVID-19, on air pollution concentrations are summarized.

Effect of the COVID-19 Lockdown on Air Pollution in the Ostrava Region

A proper estimation of anti-epidemic measures related to the influence of the COVID-19 outbreak on air quality has to deal with filtering out the weather influence on pollution concentrations. The goal of this study was to estimate the effect of anti-epidemic measures at three pollution monitoring stations in the Ostrava region. Meteorological data were clustered into groups with a similar weather pattern, and pollution data were divided into subsets according to weather patterns. Then each subset was evaluated separately. Our estimates showed a 4.1–5.7% decrease in NOx concentrations attributed to lower traffic intensity during the lockdown. The decrease of PM2.5 varied more significantly between monitoring stations. The highest decrease (4.7%) was detected at the traffic monitoring station, while there was no decrease detected at the rural monitoring station, which focuses mainly on domestic heating pollution. The key result of the study was the development of an analytical method that is able to take into account the effect of meteorological conditions. The method is much simpler and easy to replicate as an alternative to other published methods.

Natural processes dominate the pollution levels during COVID-19 lockdown over India

The lockdown measures that were taken to combat the COVID-19 pandemic minimized anthropogenic activities and created natural laboratory conditions for studying air quality. Both observations and WRF-Chem simulations show a 20–50% reduction (compared to pre-lockdown and same period of previous year) in the concentrations of most aerosols and trace gases over Northwest India, the Indo Gangetic Plain (IGP), and the Northeast Indian regions. It is shown that this was mainly due to a 70–80% increase in the height of the boundary layer and the low emissions during lockdown. However, a 60–70% increase in the pollutants levels was observed over Central and South India including the Arabian sea and Bay of Bengal during this period, which is attributed to natural processes. Elevated (dust) aerosol layers are transported from the Middle East and Africa via long-range transport, and a decrease in the wind speed (20–40%) caused these aerosols to stagnate, enhancing the aerosol levels over Central and Southern India. A 40–60% increase in relative humidity further amplified aerosol concentrations. The results of this study suggest that besides emissions, natural processes including background meteorology and dynamics, play a crucial role in the pollution concentrations over the Indian sub-continent.