Global Air Quality and COVID-19 Pandemic : Do We Breathe Cleaner Air?

The global spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has challenged most countries worldwide. It was quickly recognized that reduced activities (lockdowns) during the Coronavirus Disease of 2019 (COVID-19) pandemic produced major changes in air quality. Our objective was to assess the impacts of COVID-19 lockdowns on groundlevel PM2.5, NO2, and O-3 concentrations on a global scale. We obtained data from 34 countries, 141 cities, and 458 air monitoring stations on 5 continents (few data from Africa). On a global average basis, a 34.0% reduction in NO2 concentration and a 15.0% reduction in PM2.5 were estimated during the strict lockdown period (until April 30, 2020). Global average O-3 concentration increased by 86.0% during this same period. Individual country and continent-wise comparisons have been made between lockdown and business-as-usual periods. Universally, NO2 was the pollutant most affected by the COVID-19 pandemic. These effects were likely because its emissions were from sources that were typically restricted (i.e., surface traffic and non-essential industries) by the lockdowns and its short lifetime in the atmosphere. Our results indicate that lockdown measures and resulting reduced emissions reduced exposure to most harmful pollutants and could provide global-scale health benefits. However, the increased O-3 may have substantially reduced those benefits and more detailed health assessments are required to accurately quantify the health gains. At the same, these restrictions were obtained at substantial economic costs and with other health issues (depression, suicide, spousal abuse, drug overdoses, etc.). Thus, any similar reductions in air pollution would need to be obtained without these extensive economic and other consequences produced by the imposed activity reductions.Peer reviewe

Population exposure across central India to PM2.5 derived using remotely sensed products in a three-stage statistical model

Abstract Surface PM2.5 concentrations are required for exposure assessment studies. Remotely sensed Aerosol Optical Depth (AOD) has been used to derive PM2.5 where ground data is unavailable. However, two key challenges in estimating surface PM2.5 from AOD using statistical models are (i) Satellite data gaps, and (ii) spatio-temporal variability in AOD-PM2.5 relationships. In this study, we estimated spatially continuous (0.03° × 0.03°) daily surface PM2.5 concentrations using MAIAC AOD over Madhya Pradesh (MP), central India for 2018 and 2019, and validated our results against surface measurements. Daily MAIAC AOD gaps were filled using MERRA-2 AOD. Imputed AOD together with MERRA-2 meteorology and land use information were then used to develop a linear mixed effect (LME) model. Finally, a geographically weighted regression was developed using the LME output to capture spatial variability in AOD-PM2.5 relationship. Final Cross-Validation (CV) correlation coefficient, r2, between modelled and observed PM2.5 varied from 0.359 to 0.689 while the Root Mean Squared Error (RMSE) varied from 15.83 to 35.85 µg m−3, over the entire study region during the study period. Strong seasonality was observed with winter seasons (2018 and 2019) PM2.5 concentration (mean value 82.54 µg m−3) being the highest and monsoon seasons being the lowest (mean value of 32.10 µg m−3). Our results show that MP had a mean PM2.5 concentration of 58.19 µg m−3 and 56.32 µg m−3 for 2018 and 2019, respectively, which likely caused total premature deaths of 0.106 million (0.086, 0.128) at the 95% confidence interval including 0.056 million (0.045, 0.067) deaths due to Ischemic Heart Disease (IHD), 0.037 million (0.031, 0.045) due to strokes, 0.012 million (0.009, 0.014) due to Chronic Obstructive Pulmonary Disease (COPD), and 1.2 thousand (1.0, 1.5) due to lung cancer (LNC) during this period

Impact of nylon and teflon filter media on the sampling of inorganic aerosols over a high altitude site

Water-soluble inorganic aerosols constitute major fraction of PM2.5 play an important role in the Earth-climate system and affect adversely on human health. However, their measurement accuracy highly vary depending on choice of filter media and denuder usage. In most of the cases, denuders are either not deployed or their use is partially limited to removal of certain gases. Therefore, to understand the impact of undenuded ambient sampling and filter media type on depositional loading of water-soluble inorganic aerosols; three (1 teflon and 2 nylon based) different pore-sized filter media sampling was carried at a high altitude location in Western Ghats during winter season. Results show gas-phase adsorption was higher on nylon than hydrophobic teflon membrane. Decreased pH and overall increased conductivity observed on nylon media suggested higher adsorption of acidic species. Nylon showed increasing ionic concentration and their order followed Mg2+Na+>Ca2+>K+. ISORROPIA-II derived high NH3 and Cl(g) found on nylon than the teflon. Poor correlation between X-ray Fluorescence (XRF) and Ion Chromatography (IC) derived crustal specie (Ca) showing off-set between both the results, however, were found positively correlated for secondary species (Cl−, NH4+, NO3+ and SO42−). Neutralization efficiency revealed secondary NH4+ as the main neutralizer for secondary NO3+ and SO42− on nylon whereas on teflon, Ca2+ was found to be the main neutralizing component. On comparing Na+ and Cl+ effects over neutralization, an extensive Cl(g) adsorption on nylon than on teflon suggested prevalence of non-seasalt sources. Concentration weighted trajectory analysis for nylon minus teflon indicated an excess semi-volatiles (Cl−, NO3− and NH4+) on nylon were mostly driven by the oxidation of precursory gases originating from local and nearby coastal cities, whereas non-refractory ultrafine mode aerosols were found from inland and from majorly polluted North Indian region

Reconciliation of energy use disparities in brick production in India

Abstract Energy conservation in brick production is crucial to achieving net-zero carbon emissions from the building sector, especially in countries with major expansions in the built environment. However, widely disparate energy consumption estimates impede benchmarking its importance relative to the steel and cement industries. Here we modelled Indian brick production and its regional energy consumption by combining a nationwide questionnaire survey on feedstock, process variables and practices with remote sensing data on kiln enumeration. We found a large underreporting in current official estimates of energy consumption, with actual energy consumption comparable to that in the steel and cement industries in the country. With a total estimated production of 233 ± 15 billion bricks per year, the brick industry consumes 990 ± 125 PJ yr −1 of energy, 35 ± 6 Mt yr −1 coal and 25 ± 6 Mt yr −1 biomass. The main drivers of energy consumption for brick production are the kiln technology, the production capacity and the fuel mix used. The results suggest that improving operating practices would be a first step in making brick production more energy efficient

Heating and lighting: understanding overlooked energy-consumption activities in the Indian residential sector

Understanding the climate impact of residential emissions starts with determining the fuel consumption of various household activities. While cooking emissions have been widely studied, non-cooking energy-consumption activities in the residential sector such as heating and lighting, have been overlooked owing to the unavailability of data at national levels. The present study uses data from the Carbonaceous Aerosol Emissions, Source Apportionment and Climate Impacts (COALESCE) project, which consists of residential surveys over 6000 households across 49 districts of India, to understand the energy consumed by non-cooking residential activities. Regression models are developed to estimate information in non-surveyed districts using demographic, housing, and meteorological data as predictors. Energy demand is further quantified and distributed nationally at a 4 × 4 km resolution. Results show that the annual energy consumption from non-cooking activities is 1106 [201] PJ, which is equal to one-fourth of the cooking energy demand. Freely available biomass is widely used to heat water on traditional stoves, even in the warmer regions of western and southern India across all seasons. Space heating (51%) and water heating (42%) dominate non-cooking energy consumption. In comparison, nighttime heating for security personnel (5%), partly-residential personal heating by guards, dominant in urban centers and kerosene lighting (2%) utilize minimal energy. Biomass fuels account for over 90% of the non-cooking consumption, while charcoal and kerosene make up the rest. Half of the energy consumption occurs during winter months (DJF), while 10% of the consumption occurs during monsoon, when kerosene lighting is the highest. Firewood is the most heavily used fuel source in western India, charcoal in the northern hilly regions, agricultural residues and dung cake in the Indo-Gangetic plains, and kerosene in eastern India. The study shows that ∼20% of residential energy consumption is on account of biomass-based heating and kerosene lighting activities