PM2.5 diminution and haze events over Delhi during the COVID-19 lockdown period: an interplay between the baseline pollution and meteorology

Assessment of occupational exposure to micro/nano particles generated from carbon fiber-reinforced plastic processing

Carbon fiber-reinforced plastics (CFRP) are leading functional materials with superior strength and low mass density compared to metal. Our previous factory site analyses found that CFRP processing generates fibrous debris and fine micro/nano-sized particles of various shapes. The present interventional study was conducted at a factory located in Japan and evaluated debris consisting of various-sized particles generated during the industrial processing of CFRP, such as cutting, grinding, and turning of CFRP pipes, using real-time particle monitoring devices of the following: PM4 Digital Dust Monitor (DDM), handled Optical Particle Counter (OPC), Condensation Particle Counter (CPC), and Scanning Mobility Particle Sizer (SMPS). In addition, personal exposure of workers was evaluated using a novel wearable PM2.5-compatible device (P-sensor). First, we confirmed the presence of micro/nano particles in the dust generated during industrial processing of CFRP. Finer CFRP-generated particles were detected by the nanoparticle-compatible devices; CPC and SMPS, but not by OPC or DDM. The dynamic detection pattern of the P-sensor resembled that recorded by the nanoparticle-compatible devices. The novel wearable P-sensor can be used to measure finer particles generated by CFRP processing in occupational settings. Second, the exposure assessment was conducted twice and the levels of the micro/nano particles in the second survey were significantly (less than half) lower than that in the first survey. By avoiding immediate power-off of the exhaust system after operations, the scattering of particles was effectively reduced. Our results indicate that effective use of local exhaust ventilation system improves the workplace environment for particle exposure.

Responses of fine particulate matter (PM2.5) air quality to future climate, land use, and emission changes: Insights from modeling across shared socioeconomic pathways

Air pollution induced by fine particulate matter with diameter ≤ 2.5 μm (PM2.5) poses a significant challenge for global air quality management. Understanding how factors such as climate change, land use and land cover change (LULCC), and changing emissions interact to impact PM2.5 remains limited. To address this gap, we employed the Community Earth System Model and examined both the individual and combined effects of these factors on global surface PM2.5 in 2010 and projected scenarios for 2050 under different Shared Socioeconomic Pathways (SSPs). Our results reveal biomass-burning and anthropogenic emissions as the primary drivers of surface PM2.5 across all SSPs. Less polluted regions like the US and Europe are expected to experience substantial PM2.5 reduction in all future scenarios, reaching up to ~5 μg m-3 (70 %) in SSP1. However, heavily polluted regions like India and China may experience varied outcomes, with a potential decrease in SSP1 and increase under SSP3. Eastern China witness ~20 % rise in PM2.5 under SSP3, while northern India may experience ~70 % increase under same scenario. Depending on the region, climate change alone is expected to change PM2.5 up to ±5 μg m-3, while the influence of LULCC appears even weaker. The modest changes in PM2.5 attributable to LULCC and climate change are associated with aerosol chemistry and meteorological effects, including biogenic volatile organic compound emissions, SO2 oxidation, and NH4NO3 formation. Despite their comparatively minor role, LULCC and climate change can still significantly shape future air quality in specific regions, potentially counteracting the benefits of emission control initiatives. This study underscores the pivotal role of changes in anthropogenic emissions in shaping future PM2.5 across all SSP scenarios. Thus, addressing all contributing factors, with a primary focus on reducing anthropogenic emissions, is crucial for achieving sustainable reduction in surface PM2.5 levels and meeting sustainable pollution mitigation goals.

Aerosol-PM2.5 Dynamics: In-situ and satellite observations under the influence of regional crop residue burning in post-monsoon over Delhi-NCR, India

The increasing air pollution in the urban atmosphere is adversely impacts the environment, climate and human health. The alarming degradation of air quality, atmospheric conditions, economy and human life due to air pollution needs significant in-depth studies to ascertain causes, contributions and impacts for developing and implementing an effective policy to combat these issues. This work lies in its multifaceted approach towards comprehensive understanding and mitigating severe pollution episodes in Delhi and its surrounding areas. We investigated the aerosol dynamics in the post-monsoon season (PMS) from 2019 to 2022 under the influence of both crop residue burning and meteorological conditions. The study involves a broad spectrum of factors, including PM2.5 concentrations, active fire events, and meteorological parameters, shedding light on previously unexplored studies. The average AOD550 (0.79) and PM2.5 concentration (140.12 μg/m³) were the highest in 2019. PM2.5 was higher from mid-October to mid-November each year, exceeding the WHO guideline of 15 μg/m³ (24 h) by 27-34 times, signifying a public health emergency. A moderate to strong correlation between PM2.5 and AOD was found (r = 0.65) in 2021. The hotspot region accounts for almost 50% (2019), 47.51% (2020), 57.91% (2021) and 36.61% (2022) of the total fire events. A statistically significant negative non-linear correlation (r) was observed between wind speed (WS) and both AOD and PM2.5 concentration, influencing air quality over the region. HYSPLIT model and Windrose result show the movement of air masses predominated from the North and North-West direction during PMS. This study suggest to promotes strategies such as alternative waste management, encouraging modern agricultural practices in hot-spot regions, and enforcing strict emission norms for industries and vehicles to reducing air pollution and its detrimental effects on public health in the region and also highlights the need for future possibilities of research to attract the global attention.

Estimating black carbon levels using machine learning models in high-concentration regions

Black carbon (BC) is emitted into the atmosphere during combustion processes, often in conjunction with emissions such as nitrogen oxides (NOx) and ozone (O3), which are also by-products of combustion. In highly polluted regions, combustion processes are one of the main sources of aerosols and particulate matter (PM) concentrations, which affect the radiative budget. Despite the high relevance of this air pollution metric, BC monitoring is quite expensive in terms of instrumentation and of maintenance and servicing. With the aim to provide tools to estimate BC while minimising instrumentation costs, we use machine learning approaches to estimate BC from air pollution and meteorological parameters (NOx, O3, PM2.5, relative humidity (RH), and solar radiation (SR)) from currently available networks. We assess the effectiveness of various machine learning models, such as random forest (RF), support vector regression (SVR), and multilayer perceptron (MLP) artificial neural network, for predicting black carbon (BC) mass concentrations in areas with high BC levels such as Northern Indian cities (Delhi and Agra), across different seasons. The results demonstrate comparable effectiveness among the models, with the multilayer perceptron (MLP) showing the most promising results. In addition, the comparability between estimated and monitored BC concentrations was high. In Delhi, the MLP shows high correlations between measured and modelled concentrations during winter (R2: 0.85) and post-monsoon (R2: 0.83) seasons, and notable metrics in the pre-monsoon (R2: 0.72). The results from Agra are consistent with those from Delhi, highlighting the consistency of the neural network's performance. These results highlight the usefulness of machine learning, particularly MLP, as a valuable tool for predicting BC concentrations. This approach provides critical new opportunities for urban air quality management and mitigation strategies and may be especially valuable for megacities in medium- and low-income regions.

Decoding temporal patterns and trends of PM10 pollution over Delhi: a multi-year analysis (2015-2022)

The current study delved into an extensive analysis of multi-year observations on PM10 to have trends at various time scales in Delhi, India. High-resolution ground observations from all 37 monitoring stations from 2015 to 2022 were used. This study used non-parametric generalized additive model (GAM) based smooth-trend and Theil-Sen slope estimator techniques to analyze temporal trends and variations. The long-term PM10 concentration, both in its ambient and de-seasonalized forms, exhibited a statistically significant decreasing trend. An average decrease of - 7.57 [95% confidence interval (CI) - 16.51, 0.18] µg m-3 year-1 for ambient PM10 and - 8.45 [95% CI - 11.96, - 5.58] µg m-3 year-1 for de-seasonalized PM10 mass concentration was observed. Breaking it down into seasons, we observed significant declines in PM10 concentrations during monsoon (- 10.71 µg m-3 year-1, p < 0.1) and post-monsoon (- 7.49 µg m-3 year-1, p < 0.001). On the other hand, summer and winter displayed statistically insignificant declining trends of - 5.32 µg m-3 year-1 and - 6.06 µg m-3 year-1, respectively. Remarkably, all months except March displayed declining PM10 concentrations, suggesting a gradual reduction in particle pollution across the city. Further analysis of PM10 across various wind sectors revealed a consistent decreasing trend in all wind directions. The most substantial decrease was observed from the northwest (- 10.24 µg m-3 year-1), while the minimum reduction occurred from the east (- 5.67 µg m-3 year-1). Throughout the 8-year study period, the daily average PM10 concentration remained at 228 ± 124 µg m-3, ranging from 33 to 819 µg m-3. Seasonal variations were apparent, with concentrations during winter, summer, monsoon, and post-monsoon seasons averaging 279 ± 133, 224 ± 117, 135 ± 95, and 323 ± 142 µg m-3, respectively. November had the highest and August had the lowest concentration. Weekend PM10 concentration is slightly lower than weekdays. These findings emphasize the need for more stringent government action plans.

Analysing role of airborne particulate matter in abetting SARS-CoV-2 outbreak for scheming regional pandemic regulatory modalities

The mutating SARS-CoV-2 necessitates gauging the role of airborne particulate matter in the COVID-19 outbreak for designing area-specific regulation modalities based on the environmental state-of-affair. To scheme the protocols, the hotspots of air pollutants such as PM2.5, PM10, NH3, NO, NO2, SO2, and and environmental factors including relative humidity (RH), and temperature, along with COVID-19 cases and mortality from January 2020 till December 2020 from 29 different ground monitoring stations spanning Delhi, are mapped. Spearman correlation coefficients show a positive relationship between SARS-COV-2 with particulate matter (PM2.5 with r > 0.36 and PM10 with r > 0.31 and p-value <0·001). Besides, SARS-COV-2 transmission showed a substantial correlation with NH3 (r = 0.41), NO2 (r = 0.36), and NO (r = 0.35) with a p-value <0.001, which is highly indicative of their role in SARS-CoV-2 transmission. These outcomes are associated with the source of PM and its constituent trace elements to understand their overtone with COVID-19. This strongly validates temporal and spatial variation in COVID-19 dependence on air pollutants as well as on environmental factors. Besides, the bottlenecks of missing latent data, monotonous dependence of variables, and the role air pollutants with secondary environmental variables are discussed. The analysis set the foundation for strategizing regional-based modalities considering environmental variables (i.e., pollutant concentration, relative humidity, temperature) as well as urban and transportation planning for efficient control and handling of future public health emergencies.

Effect of lockdown and associated mobility changes amid COVID-19 on air quality in the Kathmandu Valley, Nepal

The COVID-19 pandemic caused a setback for Nepal, leading to nationwide lockdowns. The study analyzed the impact of lockdown on air quality during the first and second waves of the COVID-19 pandemic in the Kathmandu Valley. We analyzed 5 years of ground-based air quality monitoring data (2017-2021) from March to July and April to June for the first and second wave lockdowns, respectively. A significant decrease in PM2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 μm) concentrations was observed during the lockdowns. The highest rate of decline in PM2.5 levels was observed during May and July compared to the pre-pandemic year. The PM2.5 concentration during the lockdown period remained within the WHO guideline limit and NAAQS for the maximum number of days compared to the lockdown window in the pre-pandemic years (2017-2019). Likewise, lower PM2.5 levels were observed during the second wave lockdown, which was characterized by a targeted lockdown approach (smart lockdown). We found a significant correlation of PM2.5 concentration with community mobility changes (i.e., walking, driving, and using public transport) from the Spearman correlation analysis. Lockdown measures restricted human mobility that led to a lowering of PM2.5 concentrations. Our findings can be helpful in developing urban air quality control measures and management strategies, especially during high pollution episodes.

"In small places, close to home": Urban environmental impacts on child rights across four global cities

Urban environments influence child behaviours, exposures and experiences and may affect health, development, achievement and realization of fundamental human rights. We examined the status of eleven UN Convention on the Rights of the Child articles, in a multi-case study across four global cities. Within all study cities, children experienced unequal exposure to urban environmental risks and amenities. Many violations of child rights are related to car-based transportation systems and further challenged by pressures on urban systems from rapid population increases in the context of climate change. A child rights framework provides principles for a collective, multi-sectoral re-imagination of urban environments that support the human rights of all citizens.

Black carbon over tropical Indian coast during the COVID-19 lockdown: inconspicuous role of coastal meteorology

Black carbon (BC) aerosols critically impact the climate and hydrological cycle. The impact of anthropogenic emissions and coastal meteorology on BC dynamics, however, remains unclear over tropical India, a globally identified hotspot. In this regard, we have performed in situ measurements of BC over a megacity (Chennai, 12° 59' 26.5″ N, 80° 13' 51.8″ E) on the eastern coast of India during January-June 2020, comprising the period of COVID-19-induced strict lockdown. Our measurements revealed an unprecedented reduction in BC concentration by an order of magnitude as reported by other studies for various other pollutants. This was despite having stronger precipitation during pre-lockdown and lesser precipitation washout during the lockdown. Our analyses, taking mesoscale dynamics into account, unravels stronger BC depletion in the continental air than marine air. Additionally, the BC source regime also shifted from a fossil-fuel dominance to a biomass burning dominance as a result of lockdown, indicating relative reduction in fossil fuel combustion. Considering the rarity of such a low concentration of BC in a tropical megacity environment, our observations and findings under near-natural or background levels of BC may be invaluable to validate model simulations dealing with BC dynamics and its climatic impacts in the Anthropocene.

Substantial changes in Gaseous pollutants and health effects during COVID-19 in Delhi, India

Background

Coronavirus disease has affected the entire population worldwide in terms of physical and environmental consequences. Therefore, the current study demonstrates the changes in the concentration of gaseous pollutants and their health effects during the COVID-19 pandemic in Delhi, the national capital city of India.

Methodology

In the present study, secondary data on gaseous pollutants such as nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), ammonia (NH3), and ozone (O3) were collected from the Central Pollution Control Board (CPCB) on a daily basis. Data were collected from January 1, 2020, to September 30, 2020, to determine the relative changes (%) in gaseous pollutants for pre-lockdown, lockdown, and unlockdown stages of COVID-19.

Results

The current findings for gaseous pollutants reveal that concentration declined in the range of 51%-83% (NO), 40%-69% (NOx), 31%-60% (NO2), and 25%-40% (NH3) during the lockdown compared to pre-lockdown period, respectively. The drastic decrease in gaseous pollutants was observed due to restricted measures during lockdown periods. The level of ozone was observed to be higher during the lockdown periods as compared to the pre-lockdown period. These gaseous pollutants are linked between the health risk assessment and hazard identification for non-carcinogenic. However, in infants (0-1 yr), Health Quotient (HQ) for daily and annual groups was found to be higher than the rest of the exposed group (toddlers, children, and adults) in all the periods.

Conclusion

The air quality values for pre-lockdown were calculated to be "poor category to "very poor" category in all zones of Delhi, whereas, during the lockdown period, the air quality levels for all zones were calculated as "satisfactory," except for Northeast Delhi, which displayed the "moderate" category. The computed HQ for daily chronic exposure for each pollutant across the child and adult groups was more than 1 (HQ > 1), which indicated a high probability to induce adverse health outcomes.

Lockdown Effects on Air Quality in Megacities During the First and Second Waves of COVID-19 Pandemic

Air pollution is among the highest contributors to mortality worldwide, especially in urban areas. During spring 2020, many countries enacted social distancing measures in order to slow down the ongoing COVID-19 pandemic. A particularly drastic measure, the “lockdown”, urged people to stay at home and thereby prevent new COVID-19 infections during the first (2020) and second wave (2021) of the pandemic. In turn, it also reduced traffic and industrial activities. But how much did these lockdown measures improve air quality in large cities, and are there differences in how air quality was affected? Here, we analyse data from two megacities: London as an example for Europe and Delhi as an example for Asia. We consider data during first and second-wave lockdowns and compare them to 2019 values. Overall, we find a reduction in almost all air pollutants with intriguing differences between the two cities except Delhi in 2021. In London, despite smaller average concentrations, we still observe high-pollutant states and an increased tendency towards extreme events (a higher kurtosis of the probability density during lockdown) during 2020 and low pollution levels during 2021. For Delhi, we observe a much stronger decrease in pollution concentrations, including high pollution states during 2020 and higher pollution levels in 2021. These results could help to design policies to improve long-term air quality in megacities.

Artificial Neural Network Modeling on PM10, PM2.5, and NO2 Concentrations between Two Megacities without a Lockdown in Korea, for the COVID-19 Pandemic Period of 2020

The mutual relationship among daily averaged PM₁₀, PM_2.5, and NO₂ concentrations in two megacities (Seoul and Busan) connected by the busiest highway in Korea was investigated using an artificial neural network model (ANN)-sigmoid function, for a novel coronavirus (COVID-19) pandemic period from 1 January to 31 December 2020. Daily and weekly mean concentrations of NO₂ in 2020 under neither locked down cities, nor limitation of the activities of vehicles and people by the Korean Government have decreased by about 15%, and 12% in Seoul, and Busan cities, than the ones in 2019, respectively. PM ₁₀ (PM_2.5) concentration has also decreased by 15% (10%), and 12% (10%) in Seoul, and Busan, with a similar decline of NO₂, causing an improvement in air quality in each city. Multilayer perception (MLP), which has a back-propagation training algorithm for a feed-forward artificial neural network technique with a sigmoid activation function was adopted to predict daily averaged PM₁₀, PM_2.5, and NO₂ concentrations in two cities with their interplay. Root mean square error (RMSE) with the coefficient of determination (R²) evaluates the performance of the model between the predicted and measured values of daily mean PM₁₀, PM_2.5, and NO_2, in Seoul were 2.251 with 0.882 (1.909 with 0.896; 1.913 with 0.892), 0.717 with 0.925 (0.955 with 0.930; 0.955 with 0.922), and 3.502 with 0.729 (2.808 with 0.746; 3.481 with 0.734), in 2 (5; 7) nodes in a single hidden layer. Similarly, they in Busan were 2.155 with 0.853 (1.519 with 0.896; 1.649 with 0.869), 0.692 with 0.914 (0.891 with 0.910; 1.211 with 0.883), and 2.747 with 0.667 (2.277 with 0.669; 2.137 with 0.689), respectively. The closeness of the predicted values to the observed ones shows a very high Pearson r correlation coefficient of over 0.932, except for 0.818 of NO₂ in Busan. Modeling performance using IBM SPSS-v27 software on daily averaged PM₁₀, PM_2.5, and NO₂ concentrations in each city were compared by scatter plots and their daily distributions between predicted and observed values.

Frequency distribution of pollutant concentrations over Indian megacities impacted by the COVID-19 lockdown

The megacities experience poor air quality frequently due to stronger anthropogenic emissions. India had one of the longest lockdowns in 2020 to curb the spread of COVID-19, leading to reductions in the emissions from anthropogenic activities. In this article, the frequency distributions of different pollutants have been analysed over two densely populated megacities: Delhi (28.70° N; 77.10° E) and Kolkata (22.57° N; 88.36° E). In Delhi, the percentage of days with PM_2.5 levels exceeding the National Ambient Air Quality Standards (NAAQS) between 25 March and 17 June dropped from 98% in 2019 to 61% in 2020. The lockdown phase 1 brought down the PM₁₀ (particulate matter having an aerodynamic diameter ≤ 10 μm) levels below the daily NAAQS limit over Delhi and Kolkata. However, PM₁₀ exceeded the limit of 100 μgm^-3 during phases 2-5 of lockdown over Delhi due to lower temperature, weaker winds, increased relative humidity and commencement of limited traffic movement. The PM_2.5 levels exhibit a regressive trend in the highest range from the year 2019 to 2020 in Delhi. The daily mean value for PM_2.5 concentrations dropped from 85-90 μgm^-3 to 40-45 μgm^-3 bin, whereas the PM₁₀ levels witnessed a reduction from 160-180 μgm^-3 to 100-120 μgm^-3 bin due to the lockdown. Kolkata also experienced a shift in the peak of PM₁₀ distribution from 80-100 μgm^-3 in 2019 to 20-40 μgm^-3 during the lockdown. The PM_2.5 levels in peak frequency distribution were recorded in the 35-40 μgm^-3 bin in 2019 which dropped to 15-20 μgm^-3 in 2020. In line with particulate matter, other primary gaseous pollutants (NO_x, CO, SO₂, NH₃) also showed decline. However, changes in O₃ showed mixed trends with enhancements in some of the phases and reductions in other phases. In contrast to daily mean O₃, 8-h maximum O₃ showed a reduction over Delhi during lockdown phases except for phase 3. Interestingly, the time of daily maximum was observed to be delayed by ~ 2 h over Delhi (from 1300 to 1500 h) and ~ 1 h over Kolkata (from 1300 to 1400 h) almost coinciding with the time of maximum temperature, highlighting the role of meteorology versus precursors. Emission reductions weakened the chemical sink of O₃ leading to enhancement (120%; 11 ppbv) in night-time O₃ over Delhi during phases 1-3.

PM sensors as an indicator of overall air quality: Pre-COVID and COVID periods

Nowadays, there has been a substantial proliferation in the use of low-cost particulate matter (PM) sensors and facilitating as an indicator of overall air quality. However, during COVID-19 epidemics, air pollution sources have been deteriorated significantly, and given offer to evaluate the impact of COVID-19 on air quality in the world's most polluted city: Delhi, India. To address low-cost PM sensors, this study aimed to a) conduct a long-term field inter-comparison of twenty-two (22) low-cost PM sensors with reference instruments over 10-month period (evaluation period) spanning months from May 2019 to February 2020; b) trend of PM mass and number count; and c) probable local and regional sources in Delhi during Pre-CVOID (P-COVID) periods. The comparison of low-cost PM sensors with reference instruments results found with R² ranging between 0.74 and 0.95 for all sites and confirm that PM sensors can be a useful tool for PM monitoring network in Delhi. Relative reductions in PM_2.5 and particle number count (PNC) due to COVID-outbreaks showed in the range between (2-5%) and (4-13%), respectively, as compared to the P-COVID periods. The cluster analysis reveals air masses originated ∼52% from local, while ∼48% from regional sources in P-COVID and PM levels are encountered 47% and 66-70% from local and regional sources, respectively. Overall results suggest that low-cost PM sensors can be used as an unprecedented aid in air quality applications, and improving non-attainment cities in India, and that policy makers can attempt to revise guidelines for clean air.

Did unprecedented air pollution levels cause spike in Delhi's COVID cases during second wave?

The onset of the second wave of COVID-19 devastated many countries worldwide. Compared with the first wave, the second wave was more aggressive regarding infections and deaths. Numerous studies were conducted on the association of air pollutants and meteorological parameters during the first wave of COVID-19. However, little is known about their associations during the severe second wave of COVID-19. The present study is based on the air quality in Delhi during the second wave. Pollutant concentrations decreased during the lockdown period compared to pre-lockdown period (PM2.5: 67 µg m-3 (lockdown) versus 81 µg m-3 (pre-lockdown); PM10: 171 µg m-3 versus 235 µg m-3; CO: 0.9 mg m-3 versus 1.1 mg m-3) except ozone which increased during the lockdown period (57 µg m-3 versus 39 µg m-3). The variation in pollutant concentrations revealed that PM2.5, PM10 and CO were higher during the pre-COVID-19 period, followed by the second wave lockdown and the lowest in the first wave lockdown. These variations are corroborated by the spatiotemporal variability of the pollutants mapped using ArcGIS. During the lockdown period, the pollutants and meteorological variables explained 85% and 52% variability in COVID-19 confirmed cases and deaths (determined by General Linear Model). The results suggests that air pollution combined with meteorology acted as a driving force for the phenomenal growth of COVID-19 during the second wave. In addition to developing new drugs and vaccines, governments should focus on prediction models to better understand the effect of air pollution levels on COVID-19 cases. Policy and decision-makers can use the results from this study to implement the necessary guidelines for reducing air pollution. Also, the information presented here can help the public make informed decisions to improve the environment and human health significantly.

Prediction models for evaluating the impacts of ambient air pollutants on the biochemical response of selected tree species of Haridwar, India

This study aimed to assess the spatio-temporal impact of selected ambient air pollutants (SO₂, NO₂, PM₁₀, and PM_2.5) on the biochemical response of four tree species including Neem (Azadirachta indica), Mountain cedar (Toona ciliate), Bottlebrush (Callistemon citrinus), and Guava (Psidium guajava) in the province of Haridwar City, Uttarakhand, India. The study was performed in 2020 and 2021 over three selected sites (S1: institutional; S2: industrial; and S3: urban). Purposely, seasonal data of ambient air pollutants and biochemical parameters (ascorbic acid, carotenoid, chlorophyll, pH, relative water content, and dust load) of selected tree species were collected and analyzed using multiple linear regression (MLR) tool to develop prediction models. The results indicated that biochemical parameters of all tree species were negatively impacted by the polluted ambient air quality in the industrial and urban (S2 and S3) sites as compared to the non-polluted institutional (S1) site. The models were characterized by high prediction performance as indicated by the coefficient of determination (R²) values greater than 0.80. Moreover, A. indica was found to be more 'tolerant' based on the air pollution tolerance index (APTI) followed by T. ciliate, P. guajava, and C. citrinus. Similarly, the anticipated performance index (API) was reported higher for A. indica (75 to 81.25%) followed by T. ciliate (68.75 to 75.00%), P. guajava (56.25%), and C. citrinus (37.50%), respectively. This study revealed that the selected tree species are being negatively impacted by the induced pollutant exposure in the urban and industrial region of Haridwar, India which needs sufficient mitigation measures to conserve their diversities.

Fresh-marketable tomato yields enhanced by moderate weed control and suppressed fruit dehiscence with woodchip mulching

The use of plastic film imposes various environmental risks in agroecosystems. The replacement of plastics with organic materials for mulching has been suggested to enhance the sustainability of agroecosystems. However, whether woodchip mulch can be used for annual crops needs to be verified. We examined the effects of mulberry woodchip mulches on tomato-fruit yields over two successive years. Mulberry is the unique food plant of silkworms, and it will be better if its pruned shoots can be recycled rather than incinerated as waste. Setting three treatments, including woodchip mulch, weed-free and weedy (i.e., unweeded) treatments, we compared the amounts of fresh-marketable and unmarketable tomato fruits. The yields of fresh-marketable tomato fruits in the woodchip mulch treatment were significantly 16-57% higher than those in the weed-free treatment and comparable to those in the weedy treatment. The yields of unmarketable dehiscent tomato fruits in the weed-free treatment were significantly 46-86% higher than those of the other two treatments. The woodchip mulches extensively suppressed the weed density, while the grown weeds became large, preventing strong sunlight exposure and dehiscence of tomato fruits. Current results suggest that woodchips could be a possible alternative to plastics, facilitating climate change mitigation with agroforestry practices.

Assessing temporal correlation in environmental risk factors to design efficient area-specific COVID-19 regulations: Delhi based case study

Amid ongoing devastation due to Serve-Acute-Respiratory-Coronavirus2 (SARS-CoV-2), the global spatial and temporal variation in the pandemic spread has strongly anticipated the requirement of designing area-specific preventive strategies based on geographic and meteorological state-of-affairs. Epidemiological and regression models have strongly projected particulate matter (PM) as leading environmental-risk factor for the COVID-19 outbreak. Understanding the role of secondary environmental-factors like ammonia (NH3) and relative humidity (RH), latency of missing data structuring, monotonous correlation remains obstacles to scheme conclusive outcomes. We mapped hotspots of airborne PM2.5, PM10, NH3, and RH concentrations, and COVID-19 cases and mortalities for January, 2021-July,2021 from combined data of 17 ground-monitoring stations across Delhi. Spearmen and Pearson coefficient correlation show strong association (p-value < 0.001) of COVID-19 cases and mortalities with PM2.5 (r > 0.60) and PM10 (r > 0.40), respectively. Interestingly, the COVID-19 spread shows significant dependence on RH (r > 0.5) and NH3 (r = 0.4), anticipating their potential role in SARS-CoV-2 outbreak. We found systematic lockdown as a successful measure in combatting SARS-CoV-2 outbreak. These outcomes strongly demonstrate regional and temporal differences in COVID-19 severity with environmental-risk factors. The study lays the groundwork for designing and implementing regulatory strategies, and proper urban and transportation planning based on area-specific environmental conditions to control future infectious public health emergencies.

Short-term effect of COVID-19 lockdowns on atmospheric CO2, CH4 and PM2.5 concentrations in urban environment

The COVID-19 pandemic has changed all areas of human activity as it forced the authorities around the world to enact unprecedented restrictions such as "lockdowns". The low economic activity reduced the anthropogenic impact on the environment, in particular, greenhouse gases and aerosols emissions were decreased. However, the associated change in air quality is difficult to directly observe and quantify, since concentrations of these components in urban areas are affected by many other factors. In this work statistical analysis of atmospheric CO2, CH4 and PM2.5, measured in 2017-2020 in the city of Ekaterinburg, Russia, are presented. A detailed focus was made on the lockdown period from March 28 to April 30, 2020. A significant decrease in concentrations and inter-hourly variations of all studied components were observed only in the short "self-isolation" period from April 6 to April 8. The anthropogenic origin of this effect, primarily associated with the reduction in vehicular traffic, was concluded from mean diurnal cycles and air temperature correlations of all components. A decrease in the difference between measured and background CO2 and CH4 mole fractions was also found during this period. The difference was 1.3±0.2 ppm for CO2 and 8±4 ppb for CH4, which was many times lower than during any other observed periods, suggesting a short-term effect of lockdown restrictions. Overall, a negative impact on the atmosphere quickly resumed after the recovery of economic activity. The approaches in this study can be used to detect weak fluctuations of atmospheric components in other urban territories.

How Does COVID-19 Lockdown Impact Air Quality in India?

Air pollution is a severe environmental problem in the Indian subcontinent. Largely caused by the rapid growth of the population, industrialization, and urbanization, air pollution can adversely affect human health and environment. To mitigate such adverse impacts, the Indian government launched the National Clean Air Programme (NCAP) in January 2019. Meanwhile, the unexpected city-lockdown due to the COVID-19 pandemic in March 2020 in India greatly reduced human activities and thus anthropogenic emissions of gaseous and aerosol pollutants. The NCAP and the lockdown could provide an ideal field experiment for quantifying the extent to which various levels of human activity reduction impact air quality in the Indian subcontinent. Here, we study the improvement in air quality due to COVID-19 and the NCAP in the India subcontinent by employing multiple satellite products and surface observations. Satellite data shows significant reductions in nitrogen dioxide (NO2) by 17% and aerosol optical depth (AOD) by 20% during the 2020 lockdown with reference to the mean levels between 2005–2019. No persistent reduction in NO2 nor AOD is detectable during the NCAP period (2019). Surface observations show consistent reductions in PM2.5 and NO2 during the 2020 lockdown in seven cities across the Indian subcontinent, except Mumbai in Central India. The increase in relative humidity and the decrease in the planetary boundary layer also play an important role in influencing air quality during the 2020 lockdown. With the decrease in aerosols during the lockdown, net radiation fluxes show positive anomalies at the surface and negative anomalies at the top of the atmosphere over most parts of the Indian subcontinent. The results of this study could provide valuable information for policymakers in South Asia to adjust the scientific measures proposed in the NCAP for efficient air pollution mitigation.

Assessment of the coronavirus disease 2019 (COVID-19) pandemic imposed lockdown and unlock effects on black carbon aerosol, its source apportionment, and aerosol radiative forcing over an urban city in India

A nationwide lockdown was imposed in India due to the Coronavirus Disease 2019 (COVID-19) pandemic which significantly reduced the anthropogenic emissions. We examined the characteristics of equivalent black carbon (eBC) mass concentration and its source apportionment using a multiwavelength aethalometer over an urban site (Ahmedabad) in India during the pandemic induced lockdown period of year 2020. For the first time, we estimate the changes in BC, its contribution from fossil (eBC ff ) and wood (eBC wf ) fuels during lockdown (LD) and unlock (UL) periods in 2020 with respect to 2017 to 2019 (normal period). The eBC mass concentration continuously decreased throughout lockdown periods (LD1 to LD4) due to enforced and stringent restrictions which substantially reduced the anthropogenic emissions. The eBC mass concentration increased gradually during unlock phases (UL1 to UL7) due to the phase wise relaxations after lockdown. During lockdown period eBC mass concentration decreased by 35%, whereas during the unlock period eBC decreased by 30% as compared to normal period. The eBC wf concentrations were higher by 40% during lockdown period than normal period due to significant increase in the biomass burning emissions from the several community kitchens which were operational in the city during the lockdown period. The average contributions of eBC ff and eBC wf to total eBC mass concentrations were 70% and 30% respectively during lockdown (LD1 to LD4) period, whereas these values were 87% and 13% respectively during the normal period. The reductions in BC concentrations were commensurate with the reductions in emissions from transportation and industrial activities. The aerosol radiative forcing reduced significantly due to the reduction in anthropogenic emissions associated with COVID-19 pandemic induced lockdown leading to a cooling of the atmosphere. The findings in the present study on eBC obtained during the unprecedented COVID-19 induced lockdown can provide a comprehensive understanding of the BC sources and current emission control strategies, and thus can serve as baseline anthropogenic emissions scenario for future emission control strategies aimed to improve air quality and climate.

Analysis of PM2.5 and Meteorological Variables Using Enhanced Geospatial Techniques in Developing Countries: A Case Study of Cartagena de Indias City (Colombia)

The dispersion of air pollutants and the spatial representation of meteorological variables are subject to complex atmospheric local parameters. To reduce the impact of particulate matter (PM2.5) on human health, it is of great significance to know its concentration at high spatial resolution. In order to monitor its effects on an exposed population, geostatistical analysis offers great potential to obtain high-quality spatial representation mapping of PM2.5 and meteorological variables. The purpose of this study was to define the optimal spatial representation of PM2.5, relative humidity, temperature and wind speed in the urban district in Cartagena, Colombia. The lack of data due to the scarcity of stations called for an ad hoc methodology, which included the interpolation implementing an ordinary kriging (OK) model, which was fed by data obtained through the inverse distance weighting (IDW) model. To consider wind effects, empirical Bayesian kriging regression prediction (EBK) was implemented. The application of these interpolation methods clarified the areas across the city that exceed the recommended limits of PM2.5 concentrations (Zona Franca, Base Naval and Centro district), and described in a continuous way, on the surface, three main weather variables. Positive correlations were obtained for relative humidity (R2 of 0.47), wind speed (R2 of 0.59) and temperature (R2 of 0.64).

Characteristics of equivalent black carbon aerosols over Doon Valley in NW Indian Himalaya during COVID-19 lockdown 2020

Recently, black carbon (BC) has been identified as a potential transmitter for COVID-19 besides being responsible for climate change and serious health hazards. To mitigate the dreaded consequences of COVID-19 pandemic, the Government of India declared a nationwide lockdown on March 24, 2020. Accordingly, observations on equivalent black carbon (EBC) aerosols using AE 51 Aethalometer were performed during different lockdowns in Doon Valley. During April, May, June, and July, the monthly average EBC mass concentration recorded 2.12 ± 1.14 μg m^-3, 2.58 ± 1.46 μg m^-3, 2.74 ± 1.49 μg m^-3, and 2.12 ± 1.32 μg m^-3, respectively. A comparison of diurnal variation patterns with earlier studies indicates a significant reduction in EBC mass concentration levels. Bipolar NWR analysis for April and May depicts that relatively high EBC concentration was experienced with prominent south-easterly winds. The EBC concentration level during daytime was high compared to nighttime hours. Preliminary visualization of scanning electron micrographs indicates the variable morphology of aerosols. The bulk particle EDX spectral analysis indicates C, O, Na, F, Al, Si, K, Ca, and Ti elements with a dominance of C and O. Windblown dust seems to be the major contributor to the ambient aerosols. Furthermore, MODIS recorded the fire anomaly (attributed to the wheat stubble burning) starting from mid of April to early-June along the Indo-Gangetic Basin. Heavy loading of polluted aerosols was visible in CALIPSO data imageries. HYSPLIT cluster trajectories indicate that the study region is strongly influenced by the air mass transporting from the Gangetic Plain, Iran, Pakistan, Afghanistan, and Gulf region.

Variations and Source Apportionment of PM2.5 and PM10 Before and During COVID-19 Lockdown Phases in Delhi, India

Major cities across the globe including megacity Delhi have experienced considerable lower levels of air pollutants including particulate matter (PM) during COVID-19 lockdown. This study explores pre-lockdown and during lockdown air quality changes in PM_2.5, PM₁₀, PM_2.5/PM₁₀ ratio along with meteorological effects. Selected sites with different pollution signatures in Delhi including Alipur (residential), Okhla (industrial) and Pusa Road (traffic) have experienced mean (S.D.) PM_2.5 as 87.56(± 54.06), 124.45(± 73.49) and 62.14(± 58.64) µg/m³ and PM₁₀ as 163.01(± 77.37), 217.71(± 93.94) and 135.15(± 77.90) µg/m³ before lockdown (BL), while for Lockdown 1 (L1), PM_2.5 concentrations decreased drastically as 39.26(± 16.31), 38.01(± 15.16) and 31.03(± 12.79) µg/m³ and for PM₁₀ as 100.76(± 43.71), 79.47(± 30.97) and 66.53(± 22.78) µg/m³, respectively, with gradual increase in both pollutants during successive lockdown phase—Lockdown 2, Lockdown 3, Lockdown 4 and Unlock phase 1. The percentage (%) decrease in PM_2.5 (69.46%) and PM₁₀ (63.49%) during lockdown was found well correlated with people mobility (Google and Apple mobility reports), as outdoor activities showed 70–80% decrease in L1 from BL phase. Source apportionment studies suggested both local and regional pollution contribution in Delhi. Comparison of PM_2.5 and PM₁₀ concentrations for the year 2020 with that of 2018 and 2019 and study on diurnal variations of PM_2.5 and PM₁₀ have been discussed.

COVID-19 lockdown closures of emissions sources in India: Lessons for air quality and climate policy

Reduced anthropogenic activities during the COVID-19 pandemic caused significant reductions in ambient fine particulate matter (PM_2.5), SO₂ and NO_x concentrations across India. However, tropospheric O₃ concentrations spiked over many urban regions. Moreover, reductions in SO₂ and NO_x (atmospheric cooling agents) emissions unmask heating exerted by warming forcers. Basing governmental guidelines, we model daily emissions reductions in CO₂ and short-lived climate forcers (SLCFs) during different lockdown periods using bottom-up regional emission inventory. The transport sector, with maximum level of closure, followed by power plants and industry reduced nearly -50% to -75% emissions of CO₂, primary PM_2.5, SO₂ and NO_x, while warming SLCFs (black carbon, CH₄, CO and non-methane VOCs) showed insignificant reduction from continuing activity in residential and agricultural sectors. Consequently, the analysis indicates that reduction in the emission ratio of NO_x to NMVOC coincided spatially with observed increases in O₃, consistent with reduced uptake of O₃ from night-time NO_x reactions. Also, similar reductions, occurring for longer timescales (say, a year), can potentially increase the annual warming rate over India from the positive regional temperature response, estimated using climate metric. Further, by linking ongoing policies to sectoral reductions during lockdown, this study shows that the relative pacing of implementation among policies is crucial to avoid counter-productive results. A key policy recommendation is introduction and improving efficacy of programs targeting reduction of NMVOC and warming SLCF emissions (shifts away from biomass cooking technologies, household electrification and curbing open burning of crop residues), must precede the strengthening of policies targeting NO_x and SO₂ dominated sectors.

A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015-2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM_2.5, PM₁₀, PMC (coarse fraction of PM), NO₂, SO₂, NOx, CO, O₃ and the total gaseous oxidant (OX = NO₂ + O₃) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015-2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO₂ and NOx concentrations and peoples' mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO₂ and between 30% and 40% in mean PM_2.5 concentrations over 2020 full lockdown compared to the same period in 2015-2019. However, PM_2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO₂/CO ratio). Some Chinese cities showed similar increases in PM_2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O₃ concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO₂ anomalies were negative for 2020 compared to 2015-2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO₂/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO₂ decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO₂ + O₃) showed that primary NO₂ emissions at urban locations were greater than the O₃ production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO₂ and O₃ concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM_2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.

Association of air pollution and meteorological variables with the two waves of COVID-19 pandemic in Delhi: A critical analysis

Various countries across the globe have been affected by different COVID-19 waves at different points in time and with varying levels of virulence. With the backdrop of the two COVID-19 waves that broke out in Delhi, this study examines the variations in the concentrations of criteria pollutants, air quality, and meteorological variables across the waves and their influence on COVID-19 morbidity/mortality. Descriptive statistics, violin plots, and Spearman rank correlation tests were employed to assess the variations in environmental parameters and investigate their associations with COVID-19 incidence under the two waves. The susceptible-infected-recovered model and multiple linear regression were used to assess the wave-wise basic reproduction number (R0) and infection spreading trajectory of the virus. Our results show that the first wave in Delhi had three successive peaks and valleys, and the first peak of the second wave was the tallest, indicating the severity of per-day infection cases. During the analysed period (April 2020 and April 2021), concentrations of criteria pollutants varied across the waves, and air pollution was substantially higher during the second wave. In addition, the results revealed that during the second wave, NO2 maintained a significant negative relationship with COVID-19 (cases per day), while SO2 had a negative relationship with COVID-19 (cumulative cases) during the first wave. Our results also show a significant positive association of O3 with COVID-19 deaths during the first wave and cumulative cases and deaths during the second wave. The study indicates that a higher relative humidity in Delhi had a negative relation with COVID-19 cumulative cases and mortality during the first wave. The study confirms that the estimated R0 was marginally different during the two waves, and the spread of COVID-19 new cases followed a cubic growth trajectory. The findings of this study provide valuable information for policymakers in handling COVID-19 waves in various cities.

Aerosol Loading and Radiation Budget Perturbations in Densely Populated and Highly Polluted Indo-Gangetic Plain by COVID-19: Influences on Cloud Properties and Air Temperature

Aerosols emitted in densely populated and industrialized Indo-Gangetic Plain, one of the most polluted regions in the world, modulate regional climate, monsoon, and Himalayan glacier retreat. Thus, this region is important for understanding aerosol perturbations and their resulting impacts on atmospheric changes during COVID-19 lockdown period, a natural experimental condition created by the pandemic. By analyzing 5 years (2016-2020) data of aerosols and performing a radiative transfer calculation, we found that columnar and near-surface aerosol loadings decreased, leading to reductions in radiative cooling at the surface and top of the atmosphere and atmospheric warming during lockdown period. Further, satellite data analyses showed increases in cloud optical thickness and cloud-particle effective radius and decrease in lower tropospheric air temperature during lockdown period. These results indicate critical influences of COVID-19 lockdown on regional climate and water cycle over Indo-Gangetic Plain, emphasizing need for further studies from modeling perspectives.

Changes in air quality in Mexico City, London and Delhi in response to various stages and levels of lockdowns and easing of restrictions during COVID-19 pandemic

The impacts of COVID-19 lockdown restrictions have provided a valuable global experiment into the extent of improvements in air quality possible with reductions in vehicle movements. Mexico City, London and Delhi all share the problem of air quality failing WHO guideline limits, each with unique situations and influencing factors. We determine, discuss and compare the air quality changes across these cities during the COVID-19, to understand how the findings may support future improvements in their air quality and associated health of citizens. We analysed ground-level PM₁₀, PM_2.5, NO₂, O₃ and CO changes in each city for the period 1st January to August 31, 2020 under different phases of lockdown, with respect to daily average concentrations over the same period for 2017 to 2019. We found major reductions in PM₁₀, PM_2.5, NO₂ and CO across the three cities for the lockdown phases and increases in O₃ in London and Mexico City but not Delhi. The differences were due to the O₃ production criteria across the cities, for Delhi production depends on the VOC-limited photochemical regime. Levels of reductions were commensurate with the degree of lockdown. In Mexico City, the greatest reduction in measured concentration was in CO in the initial lockdown phase (40%), in London the greatest decrease was for NO₂ in the later part of the lockdown (49%), and in Delhi the greatest decrease was in PM₁₀, and PM_2.5 in the initial lockdown phase (61% and 50%, respectively). Reduction in pollutant concentrations agreed with reductions in vehicle movements. In the initial lockdown phase vehicle movements reduced by up to 59% in Mexico City and 63% in London. The cities demonstrated a range of air quality changes in their differing geographical areas and land use types. Local meteorology and pollution events, such as forest fires, also impacted the results.

The Impact of COVID-19 Lockdowns on Air Quality—A Global Review

The outbreak of the COVID-19 pandemic has emerged as a serious public health threat and has had a tremendous impact on all spheres of the environment. The air quality across the world improved because of COVID-19 lockdowns. Since the outbreak of COVID-19, large numbers of studies have been carried out on the impact of lockdowns on air quality around the world, but no studies have been carried out on the systematic review on the impact of lockdowns on air quality. This study aims to systematically assess the bibliographic review on the impact of lockdowns on air quality around the globe. A total of 237 studies were identified after rigorous review, and 144 studies met the criteria for the review. The literature was surveyed from Scopus, Google Scholar, PubMed, Web of Science, and the Google search engine. The results reveal that (i) most of the studies were carried out on Asia (about 65%), followed by Europe (18%), North America (6%), South America (5%), and Africa (3%); (ii) in the case of countries, the highest number of studies was performed on India (29%), followed by China (23%), the U.S. (5%), the UK (4%), and Italy; (iii) more than 60% of the studies included NO2 for study, followed by PM2.5 (about 50%), PM10, SO2, and CO; (iv) most of the studies were published by Science of the Total Environment (29%), followed by Aerosol and Air Quality Research (23%), Air Quality, Atmosphere & Health (9%), and Environmental Pollution (5%); (v) the studies reveal that there were significant improvements in air quality during lockdowns in comparison with previous time periods. Thus, this diversified study conducted on the impact of lockdowns on air quality will surely assist in identifying any gaps, as it outlines the insights of the current scientific research.

Unprecedented reduction in air pollution and corresponding short-term premature mortality associated with COVID-19 lockdown in Delhi, India

Countries around the world introduced strict restrictions on movement and activities known as 'lockdowns' to restrict the spread of the novel coronavirus disease (COVID-19) from the end of 2019. A sudden improvement in air quality was observed globally as a result of these lockdowns. To provide insight into the changes in air pollution levels in response to the COVID-19 restrictions we have compared surface air quality data in Delhi during four phases of lockdown and the first phase of the restriction easing period (25 March to 30 June 2020) with data from a baseline period (2018-2019). Simultaneously, short-term exposure of PM_2.5 and O₃ attributed premature mortality were calculated to understand the health benefit of the change in air quality. Ground-level observations in Delhi showed that concentrations of PM₁₀, PM_2.5 and NO₂ dropped substantially in 2020 during the overall study period compared with the same period in previous years, with average reductions of ~49%, ~39%, and ~39%, respectively. An overall lower reduction in O₃ of ~19% was observed for Delhi. A slight increase in O₃ was found in Delhi's industrial and traffic regions. The highest peak of the diurnal variation decreased substantially for all the pollutants at every phase. The decrease in PM_2.5 and O₃ concentrations in 2020, prevented 904 total premature deaths, a 60% improvement when compared to the figures for 2018-2019. The restrictions on human activities during the lockdown have reduced anthropogenic emissions and subsequently improved air quality and human health in one of the most polluted cities in the world.Implications: I am submitting herewith the manuscript entitled "Unprecedented Reduction in Air Pollution and Corresponding Short-term Premature Mortality Associated with COVID-19 Forced Confinement in Delhi, India" for potential publishing in your journal.The novelty of this research lies in: (1) we utilized ground-level air quality data in Delhi during four phases of lockdown and the first phase of unlocking period (25^th March to 30^th June) for 2020 as well as data from the baseline period (2018-2019) to provide an early insight into the changes in air pollution levels in response to the COVID-19 pandemic, (2) Chatarize the change of diurnal variation of the pollutants and (3) we assess the health risk due to PM_2.5 and O₃. Results from ground-level observations in Delhi showed that concentrations of PM₁₀, PM_2.5 and NO₂ substantially dropped in 2020 during the overall study period compared to the similar period in previous years, with an average reduction of ~49%, ~39%, and ~39%, respectively. In the case of O₃, the overall reduction was observed as ~19% in Delhi, while a slight increase was found in industrial and traffic regions. And consequently, the highest peak of the diurnal variation decreased substantially for all the pollutants. The health impact assessment of the changes in air quality indicated that 904 short-term premature deaths (~60%) were prevented due to the decline in PM_2.5 and O₃ concentrations in the study period. The restrictions on human activities during the lockdown have reduced the anthropogenic emissions and subsequently improved air quality and human health in one of the most polluted cities in the world.

Exploring short term spatio-temporal pattern of PM2.5 and PM10 and their relationship with meteorological parameters during COVID-19 in Delhi

Present study aims to examine the impact of lockdown on spatio-temporal concentration of PM2.5 and PM10 - categorized and recorded based on its levels during pre-lockdown, lockdown and unlock phases while noting the relationship of these levels with meteorological parameters (temperature, wind speed, relative humidity, rainfall, pressure, sun hour and cloud cover) in Delhi. To aid the study, a comparison was made with the last two years (2018 to 2019), covering the same periods of pre-lockdown, lockdown and unlock phases of 2020. Correlation analysis, linear regression (LR) was used to examine the impact of meteorological parameters on particulate matter (PM) concentrations in Delhi, India. The findings showed that (i) substantial decline of PM concentration in Delhi during lockdown period, (ii) there were substantial seasonal variation of particulate matter concentration in city and (iii) meteorological parameters have close associations with PM concentrations. The findings will help planners and policy makers to understand the impact of air pollutants and meteorological parameters on infectious disease and to adopt effective strategies for future.

An application of probability density function for the analysis of PM2.5 concentration during the COVID-19 lockdown period

The first Covid-19 patient in India was reported on January 30, 2020 at the state of Kerala. The patient number rose to three by February 3, 2020. In the month of March 2020, the transmissions started to increase when the people started to return back to India from the Covid-19 affected countries. On March 12, a 76-year-old man having a travel history to Saudi Arabia was the first reported fatality in India due to Covid 19. Then for the prevention of the propagation of Covid, the Indian government declared a state of health emergency and strict counter measures were taken, including locking down of cities, prohibiting almost all avoidable activities and restricting population's mobility. From March 24, 2020 due to the complete lockdown in the country, human activities were heavily restricted in the whole geographical regions of India. This pandemic lockdown eventually serves as an opportunity to observe the background concentrations of pollutants in the atmosphere. The PM 2.5 distribution can affect human health and to overcome this problem, setting up of regulation for PM is necessary. In the present study Probability density functions (PDF) method have been utilised for the investigation of PM 2.5 pollutant data distribution of five countries namely, India, China, France, Brazil and United States of America (USA) for their respective lockdown period of 2020 and corresponding same period of 2019. A detailed study has been done for India, and for that purpose India has been divided into three regions (Central India, Coastal India and Indo-Gangetic Plain (IGP)) on the basis of different meteorological conditions. PM 2.5 concentration for hourly basis has been analysed for the lockdown period 24th March to 15th June 2020 and compared with the PM 2.5 concentration of previous year 2019 for the same time period. To understand the effect of lockdown in PM 2.5 emission in India, which will give us an idea about the background concentration, PDFs (probability density functions) have also been generated for the whole year from 2015 to 2019. The "goodness-of-fit" of the probability density functions, to the data, was assessed, using various statistical indices (Chi-square test). Results show that the PM 2.5 reduction during the lockdown period of 2020 as compared to the same period of 2019 is sufficiently large. This study will give a certain degree of idea to the regulatory bodies on planning and implementation of strict air quality control plans.

Variation in chemical composition and sources of PM2.5 during the COVID-19 lockdown in Delhi

The Government of India (GOI) announced a nationwide lockdown starting 25th March 2020 to contain the spread of COVID-19, leading to an unprecedented decline in anthropogenic activities and, in turn, improvements in ambient air quality. This is the first study to focus on highly time-resolved chemical speciation and source apportionment of PM_2.5 to assess the impact of the lockdown and subsequent relaxations on the sources of ambient PM_2.5 in Delhi, India. The elemental, organic, and black carbon fractions of PM_2.5 were measured at the IIT Delhi campus from February 2020 to May 2020. We report source apportionment results using positive matrix factorization (PMF) of organic and elemental fractions of PM_2.5 during the different phases of the lockdown. The resolved sources such as vehicular emissions, domestic coal combustion, and semi-volatile oxygenated organic aerosol (SVOOA) were found to decrease by 96%, 95%, and 86%, respectively, during lockdown phase-1 as compared to pre-lockdown. An unforeseen rise in O₃ concentrations with declining NO_x levels was observed, similar to other parts of the globe, leading to the low-volatility oxygenated organic aerosols (LVOOA) increasing to almost double the pre-lockdown concentrations during the last phase of the lockdown. The effect of the lockdown was found to be less pronounced on other resolved sources like secondary chloride, power plants, dust-related, hydrocarbon-like organic aerosols (HOA), and biomass burning related emissions, which were also swayed by the changing meteorological conditions during the four lockdown phases. The results presented in this study provide a basis for future emission control strategies, quantifying the extent to which constraining certain anthropogenic activities can ameliorate the ambient air. These results have direct relevance to not only Delhi but the entire Indo-Gangetic plain (IGP), citing similar geographical and meteorological conditions common to the region along with overlapping regional emission sources. SUMMARY OF MAIN FINDINGS: We identify sources like vehicular emissions, domestic coal combustion, and semi-volatile oxygenated organic aerosol (SVOOA) to be severely impacted by the lockdown, whereas ozone levels and, in turn, low-volatility oxygenated organic aerosols (LVOOA) rise by more than 95% compared to the pre-lockdown concentrations during the last phase of the lockdown. However, other sources resolved in this study, like secondary chloride, power plants, dust-related, hydrocarbon-like organic aerosols (HOA), and biomass burning related emissions, were mainly driven by the changes in the meteorological conditions rather than the lockdown.

Nitrogen oxides concentration and emission change detection during COVID-19 restrictions in North India

COVID-19 related restrictions lowered particulate matter and trace gas concentrations across cities around the world, providing a natural opportunity to study effects of anthropogenic activities on emissions of air pollutants. In this paper, the impact of sudden suspension of human activities on air pollution was analyzed by studying the change in satellite retrieved NO2 concentrations and top-down NOx emission over the urban and rural areas around Delhi. NO2 was chosen for being the most indicative of emission intensity due to its short lifetime of the order of a few hours in the planetary boundary layer. We present a robust temporal comparison of Ozone Monitoring Instrument (OMI) retrieved NO2 column density during the lockdown with the counterfactual baseline concentrations, extrapolated from the long-term trend and seasonal cycle components of NO2 using observations during 2015 to 2019. NO2 concentration in the urban area of Delhi experienced an anomalous relative change ranging from 60.0% decline during the Phase 1 of lockdown (March 25-April 13, 2020) to 3.4% during the post-lockdown Phase 5. In contrast, we find no substantial reduction in NO2 concentrations over the rural areas. To segregate the impact of the lockdown from the meteorology, weekly top-down NOx emissions were estimated from high-resolution TROPOspheric Monitoring Instrument (TROPOMI) retrieved NO2 by accounting for horizontal advection derived from the steady state continuity equation. NOx emissions from urban Delhi and power plants exhibited a mean decline of 72.2% and 53.4% respectively in Phase 1 compared to the pre-lockdown business-as-usual phase. Emission estimates over urban areas and power-plants showed a good correlation with activity reports, suggesting the applicability of this approach for studying emission changes. A higher anomaly in emission estimates suggests that comparison of only concentration change, without accounting for the dynamical and photochemical conditions, may mislead evaluation of lockdown impact. Our results shall also have a broader impact for optimizing bottom-up emission inventories.

Aerosols optical depth and Ångström exponent over different regions in Garhwal Himalaya, India

Aerosol optical depth (AOD) and Ångström exponent (AE) are observed to be important parameters in understanding the status of ambient aerosol concentration over a particular location and depend not only upon the local but also on the large-scale dynamics of the atmosphere. The present article analyses the AOD and AE parameters retrieved with Moderate Resolution Imaging Spectrometer (MODIS) and Multi-angle Imaging Spectro-Radiometer (MISR) instruments onboard satellites, for the upper (Chamoli) and foothill (Dehradun) regions of Garhwal Himalaya in Uttarakhand, India, from 2006 to 2015. Aerosol properties are investigated at monthly, seasonal, and annual scales. The monthly mean values of MODIS-derived AOD and AE were observed to be 0.18 (± 0.14) and 1.05 (± 0.43) respectively over the Dehradun region. The seasonal maximums in AOD with MODIS and MISR were observed as 0.23 ± 0.06 and 0.29 ± 0.07 respectively in the pre-monsoon season, and the minimum values (0.099 ± 0.02) were observed in the post-monsoon season, over the Dehradun region. In contrast, in the Chamoli region, the maximum AOD (MODIS) was 0.21 ± 0.06 observed in the monsoon season and the minimum was 0.036 ± 0.007 in the post-monsoon season. Over a decade, the AE for Chamoli and Dehradun was found to vary from 0.07 to 0.17 and from 0.14 to 0.20 respectively. The median AE for Chamoli and Dehradun was found to be 1.49 and 1.47 respectively, marking the dominance of fine mode particles of anthropogenic origin. Observations show the presence of dust and polluted dust resulting from the long-range transport from the west. The comparison of AOD values from the two sensors shows a significant correlation (0.73) with slightly higher values from MISR over the year. The results obtained are important in understanding the climatic implications due to the atmospheric aerosols over the abovementioned Himalayan region of Uttarakhand, India.

Variations in Black Carbon concentration and sources during COVID-19 lockdown in Delhi

A nationwide lockdown was imposed in India due to COVID-19 pandemic in five phases from 25th March to May 31, 2020. The lockdown restricted major anthropogenic activities, primarily vehicular and industrial, thereby reducing the particulate matter concentration. This work investigates the variation in Black Carbon (BC) concentration and its sources (primarily Fossil Fuel (ff) burning and Biomass Burning (bb)) over Delhi from 18th February to July 31, 2020, covering one month of pre-lockdown phase, all the lockdown phases, and two months of successive lockdown relaxations. The daily average BC concentration varied from 0.22 to 16.92 μg/m³, with a mean value of 3.62 ± 2.93 μg/m³. During Pre-Lockdown (PL, 18th Feb-24th March 2020), Lockdown-1 (L1, 25th March-14th April 2020), Lockdown-2 (L2, 15th April-3rd May 2020), Lockdown-3 (L3, 4th-17th May 2020), Lockdown-4 (L4, 18th-31st May 2020), Unlock-1 (UN1, June 2020), and Unlock-2 (UN2, July 2020) the average BC concentrations were 7.93, 1.73, 2.59, 3.76, 3.26, 2.07, and 2.70 μg/m³, respectively. During the lockdown and unlock phases, BC decreased up to 78% compared to the PL period. The BC source apportionment studies show that fossil fuel burning was the dominant BC source during the entire sampling period. From L1 to UN2 an increasing trend in BC_ff contribution was observed (except L3) due to the successive relaxations given to anthropogenic activities. BC_ff contribution dipped briefly during L3 due to the intensive crop residue burning events in neighboring states. CWT analysis showed that local emission sources were the dominant contributors to BC concentration over Delhi.

iResponse: An AI and IoT-Enabled Framework for Autonomous COVID-19 Pandemic Management

SARS-CoV-2, a tiny virus, is severely affecting the social, economic, and environmental sustainability of our planet, causing infections and deaths (2,674,151 deaths, as of 17 March 2021), relationship breakdowns, depression, economic downturn, riots, and much more. The lessons that have been learned from good practices by various countries include containing the virus rapidly; enforcing containment measures; growing COVID-19 testing capability; discovering cures; providing stimulus packages to the affected; easing monetary policies; developing new pandemic-related industries; support plans for controlling unemployment; and overcoming inequalities. Coordination and multi-term planning have been found to be the key among the successful national and global endeavors to fight the pandemic. The current research and practice have mainly focused on specific aspects of COVID-19 response. There is a need to automate the learning process such that we can learn from good and bad practices during pandemics and normal times. To this end, this paper proposes a technology-driven framework, iResponse, for coordinated and autonomous pandemic management, allowing pandemic-related monitoring and policy enforcement, resource planning and provisioning, and data-driven planning and decision-making. The framework consists of five modules: Monitoring and Break-the-Chain, Cure Development and Treatment, Resource Planner, Data Analytics and Decision Making, and Data Storage and Management. All modules collaborate dynamically to make coordinated and informed decisions. We provide the technical system architecture of a system based on the proposed iResponse framework along with the design details of each of its five components. The challenges related to the design of the individual modules and the whole system are discussed. We provide six case studies in the paper to elaborate on the different functionalities of the iResponse framework and how the framework can be implemented. These include a sentiment analysis case study, a case study on the recognition of human activities, and four case studies using deep learning and other data-driven methods to show how to develop sustainability-related optimal strategies for pandemic management using seven real-world datasets. A number of important findings are extracted from these case studies.

A novel causality-centrality-based method for the analysis of the impacts of air pollutants on PM2.5 concentrations in China

In this paper, we analyzed the spatial and temporal causality and graph-based centrality relationship between air pollutants and PM2.5 concentrations in China from 2013 to 2017. NO2, SO2, CO and O3 were considered the main components of pollution that affected the health of people; thus, various joint regression models were built to reveal the causal direction from these individual pollutants to PM2.5 concentrations. In this causal centrality analysis, Beijing was the most important area in the Jing-Jin-Ji region because of its developed economy and large population. Pollutants in Beijing and peripheral cities were studied. The results showed that NO2 pollutants play a vital role in the PM2.5 concentrations in Beijing and its surrounding areas. An obvious causality direction and betweenness centrality were observed in the northern cities compared with others, demonstrating the fact that the more developed cities were most seriously polluted. Superior performance with causal centrality characteristics in the recognition of PM2.5 concentrations has been achieved.

The concentration of major air pollutants during the movement control order due to the COVID-19 pandemic in the Klang Valley, Malaysia

The COVID-19 pandemic forced many governments across the world to implement some form of lockdown to minimalize the spread of the virus. On 18th March 2020, the Malaysian government put into action an enforced movement control order (MCO) to reduce the numbers of infections. This study aims to investigate the concentrations of air pollutants during the MCO in the Klang Valley. The concentrations of air pollutants were recorded by the continuous air quality monitoring system (CAQMS) operated by the Department of Environment. The results showed that there were significant reductions (p < 0.05) of PM₁₀, PM_2.5, NO₂ and CO during the MCO compared with the same periods in 2019 and 2018. The highest percentage of reduction during the MCO was recorded by NO₂ with a percentage reduction of between -55 % and -72 %. O₃ concentrations at several stations showed an increase due to the reductions of its precursors such as NO. Further investigation using diurnal patterns of air pollutant concentrations both before and during the MCO showed that NO₂ and CO were both reduced significantly during the rush hours, indicating the reduction in motor vehicles on the roads as a consequence of the MCO influenced the levels of these pollutants.

Assessing the COVID-19 Impact on Air Quality: A Machine Learning Approach

The worldwide research initiatives on Corona Virus disease 2019 lockdown effect on air quality agree on pollution reduction, but the most reliable method to pollution reduction quantification is still in debate. In this paper, machine learning models based on a Gradient Boosting Machine algorithm are built to assess the outbreak impact on air quality in Quito, Ecuador. First, the precision of the prediction was evaluated by cross-validation on the four years prelockdown, showing a high accuracy to estimate the real pollution levels. Then, the changes in pollution are quantified. During the full lockdown, air pollution decreased by -53 ± 2%, -45 ± 11%, -30 ± 13%, and -15 ± 9% for NO2, SO2, CO, and PM2.5, respectively. The traffic-busy districts were the most impacted areas of the city. After the transition to the partial relaxation, the concentrations have nearly returned to the levels as before the pandemic. The quantification of pollution drop is supported by an assessment of the prediction confidence.

COVID-19 lockdown: a rare opportunity to establish baseline pollution level of air pollutants in a megacity, India

This paper analyses air quality data from megacity Delhi, India, during different periods related to the COVID-19, including pre-lockdown, lockdown and unlocked (post-lockdown) (2018-2020) to determine what baseline levels of air pollutants might be and the level of impact that could be anticipated under the COVID-19 lockdown emission scenario. The results show that air quality improved significantly during the lockdown phases, with the most significant changes occurring in the transportation and industrially dominated areas. A pronounced decline in PM_{2.5 and} PM₁₀ up to 63% and 58%, respectively, was observed during the lockdown compared to the pre-lockdown period in 2020. When compared to 2018 and 2019, they were lower by up to 51% and 61%, respectively, dropping by 56% during unlock. Some pollutants (NOx and CO) dropped significantly during lockdown, while SO₂ and O₃ declined only slightly. Moreover, when compared between the different phases of lockdown, the maximum decline for most of the pollutants and air quality index occurred during the lockdown phase 1; thus, this period was used to report the COVID-19 baseline threshold values (CBT; threshold value is the upper limit of baseline variation). Of the various statistical methods used median + 2 median absolute deviation (mMAD) was most suitable, indicating CBT values of 143 and 75 ug/m³ for PM₁₀ and PM_2.5, respectively. This results although preliminary, but it gives a positive indication that temporary lockdown can be considered as a boon to mitigate the damage we have done to the environment. Also, this baseline levels can be helpful as a first line of information to set future target limits or to develop effiective management policies for achieving better air quality in urban centres like Delhi.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13762-021-03142-3.

Ozone chemistry and dynamics at a tropical coastal site impacted by the COVID-19 lockdown

The nationwide lockdown in India to curb the spread of Coronavirus disease 2019 (COVID-19) led to colossal reduction in anthropogenic emissions. Here, we investigated the impact of lockdown on surface ozone (O₃) and nitrogen dioxide (NO₂) over a tropical coastal station – Thumba, Thiruvananthapuram (8.5°N, 76.9°E). Daytime as well as night-time NO₂ showed reduction by 0.8 (40%) and 2.3 (35%) ppbv, respectively during the lockdown period of 25–30 March 2020 as compared with the same period of previous 3 years. Unlike many urban locations, daytime surface O₃ is found to be dramatically reduced by 15 ppbv (36%) with O₃ production rate being lower by a factor of 3 during the lockdown. Interestingly, a feature of O₃-hump during the onset of land breeze typically observed during 1997–1998 has reappeared with magnitude of 5–10 ppbv. A photochemical box model, capturing this feature, revealed that significant O₃ sustained till onset of land breeze over the land due to weaker titration with NO_x during lockdown. It is suggested that the transport of this O₃ rich air with onset of land breeze led to the observed hump. Our measurements unravel a remarkable impact of the COVID-19 lockdown on the chemistry and dynamics of O₃ over this tropical coastal environment.

Reduction in concentration of PM2.5 in India's top most polluted cities: with special reference to post-lockdown period

Lockdown in India begins from 25 March and continues until 31 May 2020 due to the COVID-19 pandemic situation. Due to such an extended period of lockdown for about more than 2 months resulted in 1.38 billion populations restricted themselves from mass activities that contribute to air pollution. Thus, through our quantitative approach and trend analysis, the study aims to evaluate the changes in the level of PM_2.5 as a major pollutant for the top ten polluted cities in India, with a special emphasis on finding what happened to its concentration after the lockdown ended. Thus, to better understand the nature of variation in PM_2.5, we divide the entire 7 months into three periods for our analysis, i.e., before lockdown (1 January to 24 March), during lockdown (25 March to 31 May), and post-lockdown or unlock 1 and 2 (1 June to 31 July). Our investigation reveals that before lockdown, all the top polluted cities of India violating the national standard of PM_2.5, as the lockdown begins interestingly, all cities show a momentous reduction in PM_2.5 concentration. Further, surprisingly we found that after the post-lockdown period, the concentration of PM_2.5 was reduced to minimal, as the average concentration of PM_2.5 for all the cities is below the National Ambient Air Quality Standard (NAAQS). The study reveals that the lockdown has a consequence in improving overall air quality for the top polluted cities in India and further lockdown in the future with proper planning should be considered an alternative approach to restrain excessive emissions.

Air quality index and criteria pollutants in ambient atmosphere over selected sites:Impact and lessons to learn from COVID-19

World Health Organization (WHO) declared the disease COVID-19, caused by novel virus SARS-CoV-2, as pandemic in March 2020 following which different countries adopted immediate stringent measures to save human lives. In order to restrict the spread of disease, intra- and interstate movements of public by road/rail and air were completely halted except for those involved in essential services. India followed the option of complete lockdown (LD) in phase 1 (25th March to 14th April 2020) followed by phase 2 (15th April to 3rd May), phase 3 (4th May to 17th May), and phase 4 (18th May to 31st May 2020). Restrictions on agriculture and industrial sector were eased out in each successive phase after LD 1 and eventually country started unlock phase from 1st June to 30th June and so on. In this chapter, a discussion is made based on the data on criteria pollutants and air quality index (AQI) for selected sites in New Delhi and for selected cities across the country, respectively to spell out impacts of COVID-19 (which are initially positive) and the lessons for long-term planning for improvement of air quality. During LD 1, emissions from vehicles, industries, and construction activities were substantially down and therefore, levels of criteria pollutants CO, NOx, SO₂, Pb, O₃, PM_2.5 in ambient atmosphere came down and AQI showed improvement which was considered as silver line of the COVID-19 pandemic. Interestingly, not all criteria pollutants showed decrease to same extent and rather some still stayed above the National Ambient Air Quality Standard (NAAQS) values over New Delhi, the national capital of India. As the restrictions eased out in LD 3 and LD 4, pollution levels started increasing but again not uniformly for all pollutants and for all locations in New Delhi. During Unlock 1.0, emissions did not increase suddenly rather in a systematic linear fashion. In addition, air quality and AQI did not deteriorate much during Unlock 1.0 due to onset of monsoon rains in the country. It is suggested that all emission sources and people’s movement cannot be restricted during normal days in any country, as it has hit the socioeconomic health of nation very hard. A close examination of pollution levels and emission sources during lockdown to unlock could help us in devising a long-term strategy to bring down air pollution particularly in worst hit regions such as New Delhi. Systematic restrictions on major pollution sources with intermittent relaxations could be a possible approach to constrain air pollution.