An empirical analysis of Delhi's air quality throughout different COVID-19 pandemic waves

Delhi was one of India's COVID-19 hotspots, with significant death rates during the year 2021. This study looked at the link between COVID-19 cases in Delhi, and key meteorological variables. The study found that COVID-19 cases during the second wave (P2-March- May 2021) were much higher than during the first wave (P1-Jan-Feb 2021) in Delhi. During P1 (Jan-Feb 2021) the mean PM2.5, PM10, NO2 and CO concentrations were greater than that of P2 (March-May 2021) while the reverse happened for SO2 and O3.  Spearman correlation test indicated that COVID-19 cases maintained a significant positive correlation with the high temperature of P2 (March-May 2021) and high humidity of P1 (Jan-Feb 2021) in line with the accepted notion that COVID-19 transmitted favourably in hot and humid climates.  The Multilayer perceptron (MLP) model indicated that COVID-19 spread was supported by air pollutants and climate variables like PM2.5, NO2, RH, and WS in P1(Jan-Feb 2021) and PM2.5 and O3 in P2 (March-May 2021).  Owing to chemical coupling, across all six monitoring stations, O3 maintained an inverse relationship with NO2 throughout the COVID-19 phases in Delhi.  The city dwellers had health risks also due to PM pollution at varying degrees, indicated by high hazard quotients (HQs), requiring lowering of air pollution concentrations on an urgent basis

A GIS Model for PM10 Exposure from Biomass Burning in the North of Thailand

Human exposure to particulate matter with an aerodynamic diameter below 10 µm (PM10) is found to be associated with biomass burning in Thailand. Recent investigations confirm that primary sources of PM10 are natural forest fires and agricultural waste burning. Incidence of atmospheric haze increases significantly during the dry season from January to April. PM10 exposure in eight provinces in Northern Thailand were determined using GIS through spatial interpolation. Daily average ambient PM10 concentrations from 10 monitoring stations were used as the input data for the GIS model. Three interpolation techniques: Inverse Distance Weighted (IDW), Kriging and Spline, were compared. The predicted PM10 concentrations were verified with field measurements. GIS-based maps illustrated the variability of PM10 distribution and high-risk locations, which were found to be associated with wind direction and forest fire frequencies. Mae Hong Son, Chiang Rai and Phrae were found to be at highest risk of PM10 exposure during the dry season

PM10 and PM2.5 from Haze Smog and Visibility Effect in Chiang Mai Province Thailand

Air pollution from haze smog in Chiang Mai Thailand has become a serious problem, with fine particulate matter (FPM), PM10 and PM2.5, as the main culprits. These pollutants have serious effects on health and affect visibility in transportation and tourism. In this study, reduction in visibility was monitored using a digital camera, video records and aerial photography. Visibility in Chiang Mai was analyzed using qualitative and quantitative methods. Visibility was directly measured by GPS and Google Earth mapping. Visibility reduction from haze events was also compared by image analysis in Deciview units. Fine particulate matter concentrations and frequency of fires in Chiang Mai were associated with visibility reduction. Forest fires increased Deciview numbers. In the dry season, the frequency of fire incidents was correlated with both PM10 and PM2.5 with R2 = 0.9 (95 % CI, p < 0.05). The reverse correlation (-R2) between visual length (km) and PM10 and PM2.5 were 0.64 and 0.72 at altitude 444 m with 95 % CI, p < 0.05. The reverse correlation (-R2), at altitude 313 m was 0.93 for PM10 and 0.96 for PM2.5 with 95% CI, p < 0.05. The reverse correlation (-R2), at altitude 324 m was 0.86 for PM10 and 0.93 for PM2.5 with 95 % CI, p < 0.05. The association between visibility and FPM at low altitude was found to be more significant than at high altitude

Source Contribution of 1,3 Butadiene in the Vicinity of Petrochemical Industrial Area

Emissions and ambient concentrations of 1,3 butadiene released from the synthetic rubber industries in the largest petroleum and petrochemical complex in Thailand were evaluated in this study. The industrial emissions in this analysis were those emitted from process fugitive, combustion stack, flare, and wastewater treatment facility. It was found that wastewater treatment units were the largest emission source among other potential sources. The contribution of emission from wastewater treatment plants were about 92% of total 1,3 butadiene emission. The extent and magnitude of 1,3 butadiene in ambient air were further evaluated through the simulation of AERMOD dispersion model using these emission data together with local meteorological and topographical characteristics. Predicted annual 1,3 butadiene concentrations at every receptor were lower than its ambient air quality standard (< 0.33 μg m-3). Source apportionment analysis was performed with the objective to reveal the contribution of each emission source to the ambient concentrations at each receptor. Analytical results indicated that wastewater treatment units were the major emission source affected to the environmental concentrations of 1,3 butadiene in the study area. Evaluation of the potential adverse health impact of this chemical revealed that there may be a potential carcinogenic risk from inhalation exposure of 1,3 butadiene. Therefore, an effort in controlling emission of 1,3 butadiene should be given the priority to effectively manage the level of this compound in the environment

Relative risk pattern (95% CIs) of respiratory diseases related hospital visits in single pollutant models, Delhi.

Relative risk pattern (95% CIs) of respiratory diseases related hospital visits in single pollutant models, Delhi.</p

Exploratory variables with confidence bands and smoothers for Delhi city.

Exploratory variables with confidence bands and smoothers for Delhi city.</p

Fig 2 -

The time series of Delhi from 2016–2018 (A) PM2.5 Vs Hospital visit, (B) PM10 Vs Hospital visit, (C) RH Vs Hospital visit, (D) T Vs Hospital visit, (E) CO Vs Hospital visit, (F) PM2.5 Vs PM10.</p

Frequency distribution of PM concentrations across five seasons, Delhi.

Frequency distribution of PM concentrations across five seasons, Delhi.</p