Impact of ozone pollution on crop yield, human health, and associated economic costs in the Indo-Gangetic plains

Ozone pollution is a growing problem in many developing countries posing challenges not only to air quality but also affecting agricultural productivity and human well-being. This is the first study in the Indo-Gangetic Plain exploring how the spatial variation and severity of tropospheric ozone affect both wheat yield and all-cause mortality. We estimated that ozone-related cumulative crop production loss for wheat in selected districts of IGP was 3.4 million tonnes during the study period (2019-2021), which amounted to 923 million USD. The production-weighted Relative Yield Loss (RYL) for wheat in the IGP was 9.3 % in 2019, 12.8 % in 2020, and 11.3 % in 2021. The losses incurred in 2021 could contribute to fulfilling the wheat requirements of 11.4 million people. We also assess the health and economic gains resulting from the attainment of the World Health Organization Air Quality Guidelines (WHO AQG) for ozone concentrations. It is estimated that interventions that achieve AQG would have averted 11,407 premature deaths in 2021 translating into an impressively large health and economic gain. The annual benefits in 2021 totaled to 34 billion USD. We observe that Uttar Pradesh experienced the highest losses, both in terms of crop damage and premature deaths. Our study observes that implementing policies to prepone the planting of wheat enhances food security by mitigating yield losses. Mitigating the health impact of ambient ozone necessitates a reduction in anthropogenic emissions and to attain this objective, we propose adopting an exposure-integrated source reduction approach.

Influence of meteorological variables and air pollutants on fog/smog formation in seven major cities of Indo-Gangetic Plain

The Indo-Gangetic Plains (IGP) of the Indian subcontinent during winters experience widespread fog episodes. The low visibility is not only attributed to meteorological conditions but also to the increased pollution levels in the region. The study was carried out for Tier 1 and Tier II cities of the IGP of India, including Kolkata, Amritsar, Patiala, Hisar, Delhi, Patna, and Lucknow. This work analyzes data from 1990 to 2023 (33 years) employing the Mann-Kendall-Theil-Sen slope to determine the trends in fog occurrences and the relation between fog and meteorological parameters using multiple linear regressions. Furthermore, identifying the most relevant fog (visibility)-impacting factors from a set of both meteorological factors and air pollutants using step-wise regression. All cities indicated trend in the number of foggy days except for Kolkata. The multiple regression analysis reveals relatively low associations between fog occurrences and meteorological factors (30 to 59%), although the association was stronger when air pollution levels were considered (60 to 91%). Relative humidity, PM2.5, and PM10 have the most influence on fog formation. The study provides comprehensive insights into fog trends by incorporating meteorological data and air pollution analysis. The findings highlight the significance of acknowledging meteorological and pollution factors to understand and mitigate the impacts of reduced visibility. Hence, this information can guide policymakers, urban planners, and environmental management agencies in developing effective strategies to manage fog-related risks and improve air quality.

Abrupt exacerbation in air quality over Europe after the outbreak of Russia-Ukraine war

Much attention has been paid to the world economy and social situations in response to the outbreak of war between Russia and Ukraine in the context of COVID-19. However, much less attention has been paid to the detrimental effect of war on the atmospheric environment. Here, we used an extended deweathered-detrended technique to quantitatively evaluate changes in ambient NO2, O3, and PM2.5 AQI levels arising from emission changes (due to pandemic-driven lockdowns and war-related activities) in European cities. Results show pandemic-induced lockdowns mitigated regional air pollution in Europe, but the war activities led to an average increase of approximately 9.78% in PM2.5 and 10.07% in NO2, along with an average decrease of about 7.93% in O3 levels in cities near the war zones. Moreover, the regional air pollution exacerbated by the war activities has offset the improvements in air quality observed during the COVID-19 pandemic. The potential mechanism analysis show that the increase in atmospheric pollutant emissions driven by the war activities led to the complexity of chemical reactions in the mixed atmospheric system, which posed a huge challenge to the alleviation of air pollution in the region. This study highlights the urgent need for a ceasefire from an environmental perspective.

Source-apportionment and spatial distribution analysis of VOCs and their role in ozone formation using machine learning in central-west Taiwan

This study assessed the machine learning based sensitivity analysis coupled with source-apportionment of volatile organic carbons (VOCs) to look into new insights of O₃ pollution in Yunlin County located in central-west region of Taiwan. One-year (Jan 1 to Dec 31, 2021) hourly mass concentrations data of 54 VOCs, NO_X, and O₃ from 10 photochemical assessment monitoring stations (PAMs) in and around the Yunlin County were analyzed. The novelty of the study lies in the utilization of artificial neural network (ANN) to evaluate the contribution of VOCs sources in O₃ pollution in the region. Firstly, the station specific source-apportionment of VOCs were carried out using positive matrix factorization (PMF)-resolving six sources viz. AAM: aged air mass, CM: chemical manufacturing, IC: Industrial combustion, PP: petrochemical plants, SU: solvent use and VE: vehicular emissions. AAM, SU, and VE constituted cumulatively more than 65% of the total emission of VOCs across all 10 PAMs. Diurnal and spatial variability of source-segregated VOCs showed large variations across 10 PAMs, suggesting for distinctly different impact of contributing sources, photo-chemical reactivity, and/or dispersion due to land-sea breezes at the monitoring stations. Secondly, to understand the contribution of controllable factors governing the O₃ pollution, the output of VOCs source-contributions from PMF model along with mass concentrations of NO_X were standardized and first time used as input variables to ANN, a supervised machine learning algorithm. ANN analysis revealed following order of sensitivity in factors governing the O₃ pollution: VOCs from IC > AAM > VE ≈ CM ≈ SU > PP ≈ NO_X. The results indicated that VOCs associated with IC (VOCs-IC) being the most sensitive factor which need to be regulated more efficiently to quickly mitigate the O₃ pollution across the Yunlin County.

Integrating ambient carbonyl compounds provides insight into the constrained ozone formation chemistry in Zibo city of the North China Plain

Quantifying the impact of carbonyl compounds (carbonyls) on ozone (O₃) photochemical formation is crucial to formulating targeted O₃ mitigation strategies. To investigate the emission source of ambient carbonyls and their integrated observational constraint on the impact of O₃ formation chemistry, a field campaign was conducted in an industrial city (Zibo) of the North China Plain from August to September 2020. The site-to-site variations of OH reactivity for carbonyls were in accordance with the sequence of Beijiao (BJ, urban, 4.4 s^-1) > Xindian (XD, suburban, 4.2 s^-1) > Tianzhen (TZ, suburban, 1.6 s^-1). A 0-D box model (MCMv3.3.1) was applied to assess the O₃-precursor relationship influenced by measured carbonyls. It was found that without carbonyls constraint, the O₃ photochemical production of the three sites was underestimated to varying degrees, and the biases of overestimating the VOC-limited degree were also identified through a sensitivity test to NO_x emission changes, which may be associated with the reactivity of carbonyls. In addition, the results of the positive matrix factorization (PMF) model indicated that the main source of aldehydes and ketones was secondary formation and background (81.6% for aldehydes, 76.8% for ketones), followed by traffic emission (11.0% for aldehydes, 14.0% for ketones). Incorporated with the box model, we found that biogenic emission contributed the most to the O₃ production at the three sites, followed by traffic emission as well as industry and solvent usage. Meanwhile, the relative incremental reactivity (RIR) values of O₃ precursor groups from diverse VOC emission sources featured consistencies and differences at the three sites, which further highlights the importance of the synergetic mitigation of target O₃ precursors at regional and local scales. This study will help to provide targeted policy-guiding O₃ control strategies for other regions.

On the transition of major pollutant and O3 production regime during Covid-19 lockdowns

Lockdowns enforced amid the pandemic facilitated the evaluation of the impact of emission reductions on air quality and the production regime of O₃ under NOx reduction. Analysis of space-time variation of various pollutants (PM10, PM2.5, NOx, CO, O₃ and VOC or TNMHC) through the lockdown phases at eight typical stations (Urban/Metro, Rural/high vegetation and coastal) is carried out. It reveals how the major pollutant (PM10 or PM2.5 or O₃, or CO) differs from station to station as lockdowns progress depending on geography, land-use pattern and efficacy of lockdown implementation. Among the stations analyzed, Delhi (Chandnichowk), the most polluted (PM10 = 203 μgm^-3; O₃ = 17.4 ppbv) in pre-lockdown, experienced maximum reduction during the first phase of lockdown in PM2.5 (-47%), NO₂ (-40%), CO (-37%) while O₃ remained almost the same (2% reduction) to pre-lockdown levels. The least polluted Mahabaleshwar (PM10 = 45 μgm^-3; O₃ = 54 ppbv) witnessed relatively less reduction in PM2.5 (-2.9%), NO₂ (-4.7%), CO (-49%) while O₃ increased by 36% to pre-lockdown levels. In rural stations with lots of greenery, O₃ is the major pollutant attributed to biogenic VOC emissions from vegetation besides lower NO levels. In other stations, PM2.5 or PM10 is the primary pollutant. At Chennai, Jabalpur, Mahabaleshwar and Goa, the deciding factor of Air Quality Index (AQI) remained unchanged, with reduced values. Particulate matter, PM10 decided AQI for three stations (dust as control component), and PM2.5 decided the same for two but within acceptable limits for stations. Improvement of AQI through control of dust would prove beneficial for Chennai and Patiala; anthropogenic emission control would work for Chandani chowk, Goa and Patiala; emission control of CO is required for Mahabaleshwar and Thiruvanathapuram. Under low VOC/NOx ratio conditions, O₃ varies with the ratio, NO/NO₂, with a negative (positive) slope indicating VOC-sensitive (NOx-sensitive) regime. Peak O₃ isopleths as a function of NOx and VOC depicting distinct patterns suggest that O₃ variation is entirely non-linear for a given NOx or VOC.

Fractal analysis of impact of PM2.5 on surface O3 sensitivity regime based on field observations

Properties of PM_2.5 that can change aerosol chemistry and photolysis rates have great impacts on O₃ sensitivity regime, further affecting the production rate of surface O₃. However, responses of O₃ sensitivity regime to changes in PM_2.5 levels are difficult to be accurately determined, due to the complexity and nonlinearity of atmospheric chemistry. Here, based on long-term time series (2016-2020) of air quality variables in north and south Taiwan, fractal analysis along with Pearson correlation analysis are used to directly reveal the impacts of PM_2.5 on O₃ sensitivity regime in real atmosphere, by capturing the nonlinear dynamic relations among air pollutants. Great regional and seasonal difference in impacts of PM_2.5 on O₃ sensitivity regime may be ascribed to meteorological factors, PM_2.5 components and levels of SO₂, NO, NO₂, etc. For north Taiwan, increased PM_2.5 level can enhance the sensitivity of O₃ formation to VOC in spring and summer, whereas the opposite effect can be observed in winter. But for south Taiwan, the influence of PM_2.5 on O₃ sensitivity regime is not statistically significant, excluding spring. Furthermore, feasibility and availability of fractal analysis is tested by simulations with Empirical Kinetics Modeling Approach (EKMA). The results demonstrate the capability of fractal analysis to identify the impacts of PM_2.5 on O₃ sensitivity regime in real atmosphere, which can provide suggestions for PM_2.5-O₃ coordinated control strategies in regions suffering combined air pollution.

Ozone-3,6-dihydroxynaphtha-2,7-disulphonate chemiluminescence system is used for online ozone detection

The chemiluminescence (CL) reaction between ozone and 3,6-dihydroxynaphtha-2,7-disulphonate (DNDS) was found under alkaline conditions. Therefore, a novel CL system for ozone detection was established. The CL signal of the CL system is weak, and the CL signal is enhanced by adding nonionic surfactants. It was found that adding 16.4 g/l Triton X-100 can enhance the CL signal. The CL reagent activated by ultraviolet (UV) light produced a CL signal was nearly 10 times stronger than the CL reagent not activated by UV light; the CL signal was enhanced by adding 8 g/l NaHCO₃ to the CL reagent irradiated by UV light. Through the optimization of these test conditions, a high-selectivity, high-sensitivity online detection method for ozone CL was established. The linear range was 0.5-150 ppbv, and the limit of detection (LOD) was 0.092 ppbv (S/N = 3).

Trends in Summer-Time Tropospheric Ozone during COVID-19 Lockdown in Indian Cities Might Forecast a Higher Future Risk

High concentrations of tropospheric ozone (O3) is a serious concern in India. The generation and atmospheric dynamics of this trace gas depend on the availability of its precursors and meteorological variables. Like other parts of the world, the COVID-19 imposed lockdown and restrictions on major anthropogenic activities executed a positive impact on the ambient air quality with reduced primary pollutants/precursors load. In spite of this, several reports pointed towards a higher O3 in major Indian cities during the lockdown. The present study designed with 30 pan-Indian mega-, class I-, and class II-cities revealed critical and contrasting aspects of the geographical location, source, precursor, and meteorological variable dependency of the spatial and temporal O3 formation. This unexpected O3 increase in the major cities might forecast the probable future risks for the National Air Quality policies, especially O3 pollution management, in the Indian sub-continent. The results also pointed towards the severity of the north Indian air quality, followed by the western and eastern parts. We believe these results will definitely pave the way for researchers and policy-makers for predicting/framing regional and/or national O3 management strategies in the future.

Ammonium Chloride Associated Aerosol Liquid Water Enhances Haze in Delhi, India

The interaction between water vapor and atmospheric aerosol leads to enhancement in aerosol water content, which facilitates haze development, but its concentrations, sources, and impacts remain largely unknown in polluted urban environments. Here, we show that the Indian capital, Delhi, which tops the list of polluted capital cities, also experiences the highest aerosol water yet reported worldwide. This high aerosol water promotes secondary formation of aerosols and worsens air pollution. We report that severe pollution events are commonly associated with high aerosol water which enhances light scattering and reduces visibility by 70%. Strong light scattering also suppresses the boundary layer height on winter mornings in Delhi, inhibiting dispersal of pollutants and further exacerbating morning pollution peaks. We provide evidence that ammonium chloride is the largest contributor to aerosol water in Delhi, making up 40% on average, and we highlight that regulation of chlorine-containing precursors should be considered in mitigation strategies.

Nocturnal Atmospheric Oxidative Processes in the Indo-Gangetic Plain and Their Variation During the COVID-19 Lockdowns

This study investigates selected secondary atmospheric responses to the widely reported emission change attributed to COVID-19 lockdowns in the highly polluted Indo-Gangetic Plain (IGP) using ground-based measurements of trace gases and particulate matter. We used a chemical box-model to show that production of nighttime oxidant, NO₃, was affected mainly by emission decrease (average nighttime production rates 1.2, 0.8 and 1.5 ppbv hr^-1 before, during and relaxation of lockdown restrictions, respectively), while NO₃ sinks were sensitive to both emission reduction and seasonal variations. We have also shown that the maximum potential mixing ratio of nitryl chloride, a photolytic chlorine radical source which has not been previously considered in the IGP, is as high as 5.5 ppbv at this inland site, resulting from strong nitrate radical production and a potentially large particulate chloride mass. This analysis suggests that air quality measurement campaigns and modeling explicitly consider heterogeneous nitrogen oxide and halogen chemistry.

Interpreting the COVID effect on atmospheric constituents over the Indian region during the lockdown: chemistry, meteorology, and seasonality

Most of the published articles which document changes in atmospheric compositions during the various lockdown and unlock phases of COVID-19 pandemic have made a direct comparison to a reference point (which may be 1 year apart) for attribution of the COVID-mediated lockdown impact on atmospheric composition. In the present study, we offer a better attribution of the lockdown impacts by also considering the effect of meteorology and seasonality. We decrease the temporal distance between the impacted and reference points by considering the difference of adjacent periods first and then comparing the impacted point to the mean of several reference points in the previous years. Additionally, we conduct a multi-station analysis to get a holistic effect of the different climatic and emission regimes. In several places in eastern and coastal India, the seasonally induced changes already pointed to a decrease in PM concentrations based on the previous year data; hence, the actual decrease due to lockdown would be much less than that observed just on the basis of difference of concentrations between subsequent periods. In contrast, northern Indian stations would normally show an increase in PM concentration at the time of the year when lockdown was effected; hence, actual lockdown-induced change would be in surplus of the observed change. The impact of wind-borne transport of pollutants to the study sites dominates over the dilution effects. Box model simulations point to a VOC-sensitive composition.

Ozone Exposure and Asthma Attack in Children

BACKGROUND

Increasing evidence indicated that ozone (O₃) exposure could trigger asthma attacks in children. However, the effect of O₃ at low concentrations is uncertain.

PURPOSE

This study aimed to explore the effects of O₃ exposure at low concentrations on asthma attacks in children.

METHODS

A total of 3,475 children with asthma attacks from the First Affiliated Hospital of Xiamen University were available for the analyses. Air pollution data and meteorological data in Xiamen during 2016-2019 were also collected. A case-crossover design and conditional logistic regression models were conducted to evaluate the association between asthma attacks and outdoor air pollution with lag structures (from lag 0 to lag 6) in both single and multi-pollutant models. Furthermore, we estimated the influence of various levels of O₃ exposure on an asthma attack in three groups categorized by maximum daily 8-h sliding average ozone (O₃-8 h) (O₃-8 h ≥ 100 μg/m³, O₃-8 h: 80-99 μg/m³, O₃-8 h < 80 μg/m³).

RESULTS

For both single-pollutant models and multi-pollutant models, when O₃-8 h was higher than 80 μg/m³, O₃ exposure was increased the risk of acute asthma attacks on each day of lag. The effect of O₃ on children with asthma was significant when O₃ concentration was higher than 100 μg/m³.

CONCLUSION

O₃ concentration above 80 μg/m³ contributed to an increased risk of asthma attacks in children.

Mechanisms and Pathways for Coordinated Control of Fine Particulate Matter and Ozone

PURPOSE OF REVIEW

Fine particulate matter (PM_2.5) and ground-level ozone (O₃) pose a significant risk to human health. The World Health Organization (WHO) has recently revised healthy thresholds for both pollutants. The formation and evolution of PM_2.5 and O₃ are however governed by complex physical and multiphase chemical processes, and therefore, it is extremely challenging to mitigate both pollutants simultaneously. Here, we review mechanisms and discuss the science-informed pathways for effective and simultaneous mitigation of PM_2.5 and O₃.

RECENT FINDINGS

Global warming has led to a general increase in biogenic emissions, which can enhance the formation of O₃ and secondary organic aerosols. Reductions in anthropogenic emissions during the COVID-19 lockdown reduced PM_2.5; however, O₃ was enhanced in several polluted regions. This was attributed to more intense sunlight due to low aerosol loading and non-linear response of O₃ to NO _x . Such contrasting physical and chemical interactions hinder the formulation of a clear roadmap for clean air over such regions.

SUMMARY

Atmospheric chemistry including the role of biogenic emissions, aerosol-radiation interactions, boundary layer, and regional-scale transport are the key aspects that need to be carefully considered in the formulation of mitigation pathways. Therefore, a thorough understanding of the chemical effects of the emission reductions, changes in photolytic rates and boundary layer due to perturbation of solar radiation, and the effect of meteorological/seasonal changes are needed on a regional basis. Statistical emulators and machine learning approaches can aid the cumbersome process of multi-sector multi-species source attribution.

Probing wintertime air pollution sources in the Indo-Gangetic Plain through 52 hydrocarbons measured rarely at Delhi & Mohali

During wintertime, the Indo-Gangetic Plain suffers from severe air pollution affecting several hundred million people. Here we present unprecedented measurements and source analyses of 52 NMHCs (25 alkanes, 16 aromatics, 10 alkenes and one alkyne) in the cities of Delhi and Mohali (300 km north of Delhi) during wintertime (Dec 2016-Jan 2017). NMHCs were measured using a thermal desorption gas chromatograph equipped with flame ionisation detectors with data traceable to WMO standards. The ten most abundant NMHCs that were measured were the same at both Delhi and Mohali: propane, n-butane, acetylene, ethane, toluene, i-butane, ethene, i-pentane, benzene and propene and accounted for >50% of total measured NMHC mass concentration (137 ± 5.8 μg m^-3 in Mohali and 239 ± 7.7 μg m^-3 in Delhi). Ambient NMHCs and calculated hydroxyl radical reactivity were approximately twice as high in Delhi relative to Mohali, and 2-12 times higher than most other mega-cities, except Lahore and Karachi. Using chemical source signatures, traffic and LPG usage emissions were identified as the major contributor of these reactive NMHCs at both sites during nighttime, with additional minor contributions of garbage burning in Mohali, and evaporative fuel and biomass burning emissions in Delhi. Comparison of NMHC/CO and NMHC/C₂H₂ ratios over Mohali and Delhi, to other cities, suggested gasoline/petrol-fuelled vehicles were major NMHC emitters within the traffic source. The data from both Mohali and Delhi suggest that a large fraction of the fleet comprised vehicles with older emission control in both Mohali and Delhi. Analyses revealed poor representation of propene, ethene and trimethylbenzenes in the emission inventory (EDGARv4.3.2) over Mohali and Delhi. This study provides key data and new insights into the sources of reactive NMHCs (lifetime < few days) that drive regional wintertime pollution through direct effects and the formation of secondary pollutants.

A review on methodology in O3-NOx-VOC sensitivity study

Gaining insight into the response of surface ozone (O₃) formation to its precursors plays an important role in the policy-making of O₃ pollution control. However, the real atmosphere is an open and dissipative system, and its complexity poses a great challenge to the study of nonlinear relations between O₃ and its precursors. At present, model-based methods based on reductionism try to restore the real atmospheric photochemical system, by coupling meteorological model and chemical transport model in temporal and spatial resolution completely. Nevertheless, large inconsistencies between predictions and true values still exist, due to the great uncertainty originated from emission inventory, photochemical reaction mechanism and meteorological factors. Recently, based on field observations, some nonlinear methods have successfully revealed the complex emergent properties (long-term persistence, multi-fractal, etc) in coupling correlation between O₃ and its precursors at different time scales. The emergent properties are closely associated with the intrinsic dynamics of atmospheric photochemical system. Taking them into account when building O₃ prediction model, is helpful to reduce the uncertainty in the results. Nonlinear methods (fractal, chaos, etc) based on holism can give new insights into the nonlinear relations between O₃ and its precursors. Changes of thinking models in methodology are expected to improve the precision of forecasting O₃ concentration. This paper has reviewed the advances of different methods for studying the sensitivity of O₃ formation to its precursors during the past few decades. This review highlights that it is necessary to incorporate the emergent properties obtained by nonlinear methods into the modern models, for assessing O₃ formation under combined air pollution environment more accurately. Moreover, the scaling property of coupling correlation detected in the real observations of O₃ and its precursors could be used to test and improve the simulation performance of modern models.

Exploring the potential of machine learning for simulations of urban ozone variability

Machine learning (ML) has emerged as a powerful technique in the Earth system science, nevertheless, its potential to model complex atmospheric chemistry remains largely unexplored. Here, we applied ML to simulate the variability in urban ozone (O₃) over Doon valley of the Himalaya. The ML model, trained with past variations in O₃ and meteorological conditions, successfully reproduced the independent O₃ data (r² ~ 0.7). Model performance is found to be similar when the variation in major precursors (CO and NO_x) were included in the model, instead of the meteorology. Further the inclusion of both precursors and meteorology improved the performance significantly (r² = 0.86) and the model could also capture the outliers, which are crucial for air quality assessments. We suggest that in absence of high-resolution measurements, ML modeling has profound implications for unraveling the feedback between pollution and meteorology in the fragile Himalayan ecosystem.

Importance of meteorology and chemistry in determining air pollutant levels during COVID-19 lockdown in Indian cities

Indian cities can experience severe air pollution, and the reduction in activity during the first national COVID-19 lockdown (2020) offered a natural experiment to study the contribution of local sources. The current work aimed to quantify the changes due to the lockdown in NO_x, O₃ and PM_2.5 in two contrasting cities in India (Delhi and Hyderabad) using a boosted regression tree model to account for the influence of meteorology. The median NO_x and PM_2.5 concentrations were observed to decrease after lockdown in both cities, up to 57% and 75% for PM_2.5 and NO_x, respectively when compared to previous years. After normalization due to meteorology the calculated reduction after lockdown for PM_2.5 was small (<8%) in both cities, and was likely less attributable to changes in local emissions, but rather due changes in background levels (i.e. regional source(s)). The reduction of NO_x due to lockdown varied by site (on average 5-30%), likely reflecting differences in relative proximity of local sources to the monitoring site, demonstrating the key influence of meteorology on ambient levels post-lockdown. Ozone was observed to increase after lockdown at both sites in Delhi, likely due to changes in relative amounts of precursor concentrations promoting ozone production, suggesting a volatile organic compound (VOC)-limited regime in Delhi. Thus, the calculated reduction in air pollutants due to lockdown in the current work cannot be extrapolated to be solely from a reduction in emissions and instead reflects the overall change in ambient levels, as meteorology and atmospheric chemical processes also contributed.

Sources of PM2.5 and its responses to emission reduction strategies in the Central Plains Economic Region in China: Implications for the impacts of COVID-19

The Central Plains Economic Region (CPER) located along the transport path to the Beijing-Tianjin-Hebei area has experienced severe PM_2.5 pollution in recent years. However, few modeling studies have been performed on the sources of PM_2.5, especially the impacts of emission reduction strategies. In this study, the Nested Air Quality Prediction Model System (NAQPMS) with an online tracer-tagging module was adopted to investigate source sectors of PM_2.5 and a series of sensitivity tests were conducted to investigate the impacts of different sector-based mitigation strategies on PM_2.5 pollution. The response surfaces of pollutants to sector-based emission changes were built. The results showed that resident-related sector (resident and agriculture), fugitive dust, traffic and industry emissions were the main sources of PM_2.5 in Zhengzhou, contributing 49%, 19%, 15% and 13%, respectively. Response surfaces of pollutants to sector-based emission changes in Henan revealed that the combined reduction of resident-related sector and industry emissions efficiently decreased PM_2.5 in Zhengzhou. However, reduced emissions in only the Henan region barely satisfied the national air quality standard of 75 μg/m³, whereas a 50%-60% reduction in resident-related sector and industry emissions over the whole region could reach this goal. On severely polluted days, even a 60% reduction in these two sectors over the whole region was insufficient to satisfy the standard of 75 μg/m³. Moreover, a reduction in traffic emissions resulted in an increase in the O₃ concentration. The results of the response surface method showed that PM_2.5 in Zhengzhou decreased by 19% in response to the COVID-19 lockdown, which approached the observed reduction of 21%, indicating that the response surface method could be employed to study the impacts of the COVID-19 lockdown on air pollution. This study provides a scientific reference for the formulation of pollution mitigation strategies in the CPER.

Phase-wise analysis of the COVID-19 lockdown impact on aerosol, radiation and trace gases and associated chemistry in a tropical rural environment

Phase-wise variations in different aerosol (BC, AOD, PM₁, PM_2.5 and PM₁₀), radiation (direct and diffused) and trace gases (NO, NO₂, CO, O₃, SO₂, CO₂ and CH₄) and their associated chemistry during the COVID-19 lockdown have been investigated over a tropical rural site Gadanki (13.5° N, 79.2° E), India. Unlike most of the other reported studies on COVID-19 lockdown, this study provides variations over a unique tropical rural environment located at a scientifically strategic location in the Southern Indian peninsula. Striking differences in the time series and diurnal variability have been observed in different phases of the lockdown. The levels of most species that are primarily emitted from anthropogenic activities reduced significantly during the lockdown which also impacted the levels and diurnal variability of secondary species like O₃. When compared with the same periods in 2019, short-lived trace gas species such as NO, NO₂, SO₂ which have direct anthropogenic emission influence have shown the reduction over 50%, whereas species like CO and O₃ which have direct as well as indirect impacts of anthropogenic emissions have shown reductions up to 10%. Long-lived species (CO₂ and CH₄) have shown negligible difference (<1%). BC and AOD have shown reductions over 20%. Particulate Matter (1, 2.5 and 10) reductions have been in the range of 40 to 50% when compared to the pre-lockdown period. The changes in shortwave downward radiation at the surface, diffuse component due to the scattering and diffuse fraction have been +2.2%, -4.1% and -2.4%, respectively, in comparison with 2019. In contrast with the studies over urban environments, air quality category over the rural environment remained same during the lockdown despite reduction in pollutants level. All the variations observed for different species and their associated chemistry provides an excellent demonstration of rural atmospheric chemistry and its intrinsic links with the precursor concentrations and dynamics.