Air Pollution Episodes Research Articles

Compound dry-hot-fire events connecting Central and Southeastern South America: an unapparent and deadly ripple effect

South America has experienced severe compound drought-heatwaves (CDHW), exacerbating fires. Recently, the unprecedented Pantanal 2020 fire season (P20F), burning a third of the biome, resulted in well-reported local impacts on the ecosystem, economy, and health. Nevertheless, the long-range ripple effects of this event remain unknown. We investigated the P20F-related cascading hazards, integrating models, observational and satellite-based data. P20F-related smoke elevated PM2.5 levels in the SA’s most populated area, exceeding WHO guidelines by up to 600%. Smoke-induced air pollution episodes coincided with widespread heatwaves, amplifying health risks. The mortality burden attributable to this multi-hazard short-term (14 days) exposure was estimated to be 2150 premature deaths (21% increase above expected levels). Our findings highlight that the impacts of CDHW-fires in SA are beyond the local level, implying growing challenges for risk management and public health and the need for governance based on telecoupled flows, linking different systems over multiple scales.

Varying Performance of Low-Cost Sensors During Seasonal Smog Events in Moravian-Silesian Region

Air pollution monitoring in industrial regions like Moravia-Silesia faces challenges due to complex environmental conditions. Low-cost sensors offer a promising, cost-effective alternative for supplementing data from regulatory-grade air quality monitoring stations. This study evaluates the accuracy and reliability of a prototype node containing low-cost sensors for carbon monoxide (CO) and particulate matter (PM), specifically tailored for the local conditions of the Moravian-Silesian Region during winter and spring periods. An analysis of the reference data observed during the winter evaluation period showed a strong positive correlation between PM, CO, and NO2 concentrations, attributable to common pollution sources under low ambient temperature conditions and increased local heating activity. The Sensirion SPS30 sensor exhibited high linearity during the winter period but showed a systematic positive bias in PM10 readings during Polish smog episodes, likely due to fine particles from domestic heating. Conversely, during Saharan dust storm episodes, the sensor showed a negative bias, underestimating PM10 levels due to the prevalence of coarse particles. Calibration adjustments, based on the PM1/PM10 ratio derived from Alphasense OPC-N3 data, were initially explored to reduce these biases. For the first time, this study quantifies the influence of particle size distribution on the SPS30 sensor’s response during smog episodes of varying origin, under the given local and seasonal conditions. In addition to sensor evaluation, we analyzed the potential use of data from the Copernicus Atmospheric Monitoring Service (CAMS) as an alternative to increasing sensor complexity. Our findings suggest that, with appropriate calibration, selected low-cost sensors can provide reliable data for monitoring air pollution episodes in the Moravian-Silesian Region and may also be used for future adjustments of CAMS model predictions.

Understanding Meteorological Factors Influencing Heavy Air Pollution in Guwahati, India

Guwahati, Assam's largest city and the gateway to Northeast India faces rapid urbanization and increasing pollution. Although heavy air pollution has been frequent in recent decades, the exact reason behind this is still uncertain. During the study period from 2021 to 2023, we identified eight instances of heavy pollution events across the region, seven of which are aligned with a high-pressure system and influenced by westerly airflow at 700 hPa. Apart from that, temperature inversion, low boundary layer height, and low horizontal wind speeds in the lower troposphere help confine pollution, resulting in insufficient mixing and dispersion of pollutants in the atmosphere, which favors the development of heavy air pollution episodes. We investigated the impact of local and regional emissions on PM10 and PM2.5 concentrations over Guwahati City using the regional Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Our finding revealed that regional transport significantly contributes to PM10 (57.3 % to 75.5 %) and PM2.5 (56.9 % to 73.5 %) levels, highlighting the predominance of regionally transported pollutants. During high pollution days, local emissions increase due to stagnant air, limited dispersion from high-pressure systems, and temperature inversions, trapping pollutants in the region. This research offers valuable scientific knowledge to anticipate heavy air pollution episodes in Guwahati.

Multiscale CFD analysis of urban air pollution dome and ventilation enhancement via an urban chimney

Air pollution episodes are critical environmental challenges in urban areas, primarily linked to the lack of ventilation during calm and stable atmospheric conditions, rather than a significant increase in emissions. The prevailing circulation pattern under these conditions is the Urban Heat Island Circulation (UHIC), which governs the characteristics of the urban pollution dome and the distribution of pollutants. This study aims to assess the UHIC's influence on spatial and temporal variations in pollutant concentrations and to investigate the efficiency of an Urban Chimney (UC) in improving air quality. Due to the interaction of micro- and meso-scale flows, a multi-scale analysis is required. For this purpose, a pressure-based CFD solver is adapted for analyzing airflow and pollutant dispersion in a multi-scale environment. A coordinate transformation method is used to account for meso-scale effects, and the resulting source terms are applied to the solver. The species transport equation is transformed to the new coordinate system, and the effects of species diffusion on enthalpy transport are included in the governing equations. The results demonstrate that UHIC reduces the average pollutant concentration and causes pollution peaks near ground level and at the city center. The interaction of micro-scale flow within the chimney with meso-scale UHIC impacts the flow field and pollutant concentration across the entire domain, decreasing the urban plume's vertical velocity by 40 percent and the mixing height at the city center by 20 percent. It also increases the vertical velocity within the UC by 23 percent. The UC can serve as a controlling tool for urban ventilation, and its capacity to remove pollutants increases sixfold when interacting with UHIC, reducing pollutant concentration citywide.

Integrating Doppler LiDAR and machine learning into land-use regression model for assessing contribution of vertical atmospheric processes to urban PM2.5 pollution

Air pollution has been recognized as a global issue, through adverse effects on environment and health. While vertical atmospheric processes substantially affect urban air pollution, traditional epidemiological research using Land-use regression (LUR) modeling usually focused on ground-level attributes without considering upper-level atmospheric conditions. This study aimed to integrate Doppler LiDAR and machine learning techniques into LUR models (LURF-LiDAR) to comprehensively evaluate urban air pollution in Hong Kong, and to assess complex interactions between vertical atmospheric processes and urban air pollution from long-term (i.e., annual) and short-term (i.e., two air pollution episodes) views in 2021. The results demonstrated significant improvements in model performance, achieving CV R2 values of 0.81 (95 % CI: 0.75–0.86) for the long-term PM2.5 prediction model and 0.90 (95 % CI: 0.87–0.91) for the short-term models. Approximately 69 % of ground-level air pollution arose from the mixing of ground- and lower-level (105 m–225 m) particles, while 21 % was associated with upper-level (825 m–945 m) atmospheric processes. The identified transboundary air pollution (TAP) layer was located at ~900 m above the ground. The identified Episode one (E1: 7 Jan–22 Jan) was induced by the accumulation of local emissions under stable atmospheric conditions, whereas Episode two (E2: 13 Dec–24 Dec) was regulated by TAP under instable and turbulent conditions. Our improved air quality prediction model is accurate and comprehensive with high interpretability for supporting urban planning and air quality policies.

Determination of Fine Particulate Matter Episode Periods in Jakarta by Incorporating Meteorological Conditions

The analysis of observed fine particulate matter (PM2.5) in Jakarta, Indonesia, was conducted from January 2021 to October 2023 using hourly monitoring data from AirNow, managed by the U.S. Environmental Protection Agency. Data were collected from two air quality monitoring stations in Central and South Jakarta. Meteorological data, including wind speed, temperature, humidity, and rainfall, were obtained from NASA's global forecasting database. The objective was to understand the correlation between PM2.5 formation and meteorological conditions to identify favorable meteorology during high air pollution episodes. During the dry season (May to August), there was a notable increase in PM2.5, linked to higher temperatures, lower humidity, slower wind speeds, and minimal rainfall. The results showed that meteorological conditions significantly affected PM2.5 concentrations, influenced by seasonal changes. In contrast, the rainy season had lower PM2.5 concentrations due to higher rainfall, humidity, and wind speed, and lower temperatures. Further modeling studies are required to assess the combined impacts of emission build-up and meteorology on recurring PM2.5 episodes in Jakarta.

Hazardous wildfire smoke events can alter dawn soundscapes in dry forests of central and eastern Washington, United States

As global wildfire activity increases, wildlife are facing greater exposure to hazardous smoke pollution – with unknown consequences for biodiversity. Research on the effects of smoke on wild animals is extremely limited, in part due to the inherent logistical challenges of observing how animals respond to smoke in real time. Passive acoustic monitoring may be a powerful tool to safely and effectively monitor biodiversity before, during, and after major smoke events. In this study, we used data collected from a large-scale network of bioacoustic recorders at 92 sites in central and eastern Washington state during August–September, 2019–2020 to investigate the effect of wildfire smoke on dawn soundscapes and, by extension, acoustically active wildlife. We used acoustic indices to document and characterize changes in soundscapes related to smoke exposure, including the Acoustic Complexity Index (ACI), Bioacoustic Index (BI), and Normalized Difference Soundscape Index (NDSI). Higher values of these indices likely indicate higher levels of biodiversity in our study area. We hypothesized that wildfire smoke would reduce bird vocalizations, leading to declines in ACI, BI, and NDSI at dawn, when birds are most active. We used linear and quantile regression models to test for an effect of daily exposure to fine particulate matter (PM2.5), a marker of wildfire smoke, on the mean daily values and the upper 90th percentile of each index at dawn. We also conducted a before-during-after analysis of a particularly hazardous smoke event that impacted our study area on September 12–14, 2020. We did not observe linear effects of daily PM2.5 on average or peak daily values of acoustic indices; however, we did observe a significant reduction in ACI and BI during the three-day smoke event in 2020 and in the two weeks following this air pollution episode. Our results indicate that, on average, ACI and BI were reduced by 2.7 % and 15.9 % during and 1.5 % and 11.0 % afterward, respectively. These findings add further evidence that wildfire smoke alters soundscapes, likely due to changes in the presence, abundance, or behavior of acoustically active animals. Furthermore, our study demonstrates that wildfire smoke may have delayed and/or cumulative effects on acoustically active wildlife. Our study highlights the potential for passive acoustic monitoring to document wildlife responses to smoke pollution and identify potentially relevant exposure periods.

Knowledge, Attitudes, and Practices Regarding Air Pollution among Medical Students.

Background: Air pollution has emerged as a global public health concern. Specifically, in Medellín, Colombia, episodes of elevated air pollution have been documented. Medical students' knowledge of air pollution is paramount for implementing future interventions directed toward patients. The aim of this research was to delineate the knowledge, attitudes, and practices regarding air pollution among medical students at a private university in Medellín. Methods: A cross-sectional study involving 352 medical students was conducted. A questionnaire was administered, generating scores ranging from 0 to 100, where a higher score signified better knowledge, attitudes, and practices. Data were analyzed using frequencies, summary measures, non-parametric tests, and linear regression. Results: In total, 31% rated the education received at the university on the relationship between health and air quality as fair to poor, and 81% perceived the air quality in the city as poor. The knowledge score was 77.8 (IQR 71.1-85.6), with 90% acknowledging that exposure to air pollution increases the risk of various diseases. The attitudes score was 82.1 (IQR 71.8-87.2), and 25.9% believed that air pollution is a multifactorial problem, rendering their actions ineffective. In terms of practices, the score was 50 (IQR 42.9-57.1), indicating that students either did not employ protective measures against pollution or used inappropriate practices such as masks or air purifiers. Regression analysis revealed no association between knowledge and practices. Conclusion: The findings of this study underscore that medical students possess commendable knowledge regarding the health effects of air pollution. However, their adoption of inappropriate practices for self-protection is evident. The lack of correlation between knowledge and practices highlights the necessity of educational initiatives to be complemented by regulatory and cultural interventions.

Hotspot driven air pollution during crop residue burning season in the Indo-Gangetic Plain, India

Intensive crop residue burning (CRB) in northern India triggers severe air pollution episodes over the Indo-Gangetic Plain (IGP) each year during October and November. We have quantified the contribution of hotspot districts (HSDs) and total CRB to poor air quality over the IGP. Initially, we investigated the spatiotemporal distribution of CRB fire within the domain and pinpointed five HSD in each Punjab and Haryana. Furthermore, we have simulated air quality and quantified the impact of CRB using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), incorporating recent anthropogenic emissions (EDGAR v5) and biomass burning emissions (FINN v2.4) inventories, along with MOZART-MOSAIC chemistry. The key finding is that HSDs contributed ∼80% and ∼50% of the total fire counts in Haryana and Punjab, respectively. The model effectively captured observed PM₂.₅ concentrations, with a normalized mean bias (NMB) below 0.2 and R-squared (R2) exceeding 0.65 at the majority of validation sites. However, some discrepancies were observed at a few sites in Delhi, Punjab, Haryana, and West Bengal. The National Capital Region experienced the highest PM₂.₅ concentrations, followed by Punjab, Haryana, Uttar Pradesh, Bihar, and West Bengal. Moreover, HSDs were responsible for about 70% of the total increase in CRB-induced PM₂.₅ in the western, central, and eastern cities, and around 50% in the northern cities. By eliminating CRB emissions across the domain, we could potentially save approximately 18,000 lives annually. Policymakers, scientists, and institutions can leverage the framework to address air pollution at national and global scales by targeting source-specific hotspots. This approach, coupled with appropriate technological and financial solutions, can contribute to achieving climate change and sustainable development goals.

The health risk reduction of PM2.5 via a green curtain system in Taiwan

Green infrastructure can release oxygen, and capture particulate matter (PM), which can improve urban air quality and heat islands. This study aims to develop a green curtain to reduce PM2.5 through three-dimensional greening with a low preparation cost, low maintenance cost, high efficiency, and readily replaceable types. In this study, a green curtain system (GCS) was installed on a balcony with three plants, Chlorophytum comosum, Nephrolepis exaltata, and Nephrolepis pendula (Raddi) J. Sm, Another balcony without GCS was used as a control group for the study.The experiment demonstrated the efficacy of Greening Curtain Systems (GCS) on balconies in reducing PM2.5 exceedance time, particularly with 60 pots containing a mix of three plants, where Chlorophytum comosum showed notable effectiveness as a single protective plant. Leaf capture efficiency remained relatively unchanged even after 17 days without washing, attributed to the humidity of winter evenings and the evapotranspiration effect of the plants.Additionally, a health risk assessment, employing an attributable risk approach, was conducted to validate the effectiveness of PM protection measures in lowering occupants' health risks. Utilizing data on the correlation between all-cause mortality risk in individuals aged 65 and above and considering the delayed impact of atmospheric factors on human health, we employed a delayed nonlinear model to explore the exposure-response relationship. The results revealed that GCS successfully reduced the duration of high PM2.5 concentration during air pollution episodes by 15–18 μg/m3, thereby mitigating potential health risks associated with successive air pollution episodes.

Global health impacts of ambient fine particulate pollution associated with climate variability

Air pollution is a key global environmental problem raising human health concern. It is essential to comprehensively assess the long-term characteristics of air pollution and the resultant health impacts. We first assessed the global trends of fine particulate matter (PM2.5) during 1980–2020 using a monthly global PM2.5 reanalysis dataset, and evaluated their association with three types of climate variability including El Niño-Southern Oscillation, Indian Ocean Dipole and North Atlantic Oscillation. We then estimated PM2.5-attributable premature deaths using integrated exposure–response functions. Results show a significant increasing trend of ambient PM2.5 during 1980–2020 due to increases in anthropogenic emissions. Ambient PM2.5 caused a total of ∼ 135 million premature deaths globally during the four decades. Occurrence of air pollution episodes was strongly associated with climate variability, which were associated with up to 14 % increase in annual global PM2.5-attributable premature deaths.

Source emission contributions to particulate matter and ozone, and their health impacts in Southeast Asia

Southeast Asia has been experiencing severe air pollution due to its substantial local emissions and transboundary air pollution (TAP), causing significant health impacts. While literature focused on air pollution episodes in Southeast Asia, we have yet to fully understand the contributions of local emission sectors and TAP to air quality in the region annually. Herein we employed air quality modeling with the species tagging method to first assess the contributions of source sectors and locations to fine particulate matter (PM2.5) and ozone (O3) in Southeast Asia and to hence quantify the resultant health impacts. Our results show that air pollutant exposure was associated with ∼ 900 thousand premature mortalities in Southeast Asia every year. Of which, 77 % and 23 % were due to local emissions and TAP in the region, respectively. ∼ 87 % of the premature mortalities due to local emissions were induced by PM2.5 exposure, whereas the remaining were due to O3 exposure. PM2.5-related health impacts were dominated by industrial (45 %) and residential (17 %) emissions, and O3-related impacts were mainly due to biogenic (40 %) and road transport (24 %) emissions. Furthermore, the health impacts of TAP were particularly adverse in Brunei, East Timor, Singapore, Laos, and border regions.

Evaluation of WRF-Chem-simulated meteorology and aerosols over northern India during the severe pollution episode of 2016

Abstract. We use a state-of-the-art regional chemistry transport model (WRF-Chem v4.2.1) to simulate particulate air pollution over northern India during September–November 2016. This period includes a severe air pollution episode marked by exceedingly high levels of hourly PM2.5 (particulate matter having an aerodynamic diameter ≤ 2.5 µm) during 30 October to 7 November, particularly over the wider Indo-Gangetic Plain (IGP). We provide a comprehensive evaluation of simulated seasonal meteorology (nudged by ERA5 reanalysis products) and aerosol chemistry (PM2.5 and its black carbon (BC) component) using a range of ground-based, satellite and reanalysis products, with a focus on the November 2016 haze episode. We find the daily and diurnal features in simulated surface temperature show the best agreement followed by relative humidity, with the largest discrepancies being an overestimate of night-time wind speeds (up to 1.5 m s−1) confirmed by both ground and radiosonde observations. Upper-air meteorology comparisons with radiosonde observations show excellent model skill in reproducing the vertical temperature gradient (r>0.9). We evaluate modelled PM2.5 at 20 observation sites across the IGP including eight in Delhi and compare simulated aerosol optical depth (AOD) with data from four AERONET sites. We also compare our model aerosol results with MERRA-2 reanalysis aerosol fields and MODIS satellite AOD. We find that the model captures many features of the observed aerosol distributions but tends to overestimate PM2.5 during September (by a factor of 2) due to too much dust, and underestimate peak PM2.5 during the severe episode. Delhi experiences some of the highest daily mean PM2.5 concentrations within the study region, with dominant components nitrate (∼25 %), dust (∼25 %), secondary organic aerosols (∼20 %) and ammonium (∼10 %). Modelled PM2.5 and BC spatially correlate well with MERRA-2 products across the whole domain. High AOD at 550nm across the IGP is also well predicted by the model relative to MODIS satellite (r≥0.8) and ground-based AERONET observations (r≥0.7), except during September. Overall, the model realistically captures the seasonal and spatial variations of meteorology and ambient pollution over northern India. However, the observed underestimations in pollutant concentrations likely come from a combination of underestimated emissions, too much night-time dispersion, and some missing or poorly represented aerosol chemistry processes. Nevertheless, we find the model is sufficiently accurate to be a useful tool for exploring the sources and processes that control PM2.5 levels during severe pollution episodes.

Solving the mysteries of Lahore smog: the fifth season in the country

South Asian smog is an annually recurring air pollution event that is characterized by high concentrations of air pollutants, low visibility, and severe socio-economic disruptions. It is most frequently observed across the north-western parts of the Indo-Gangetic Plains (IGP). Since 2016, it has become so frequent and pervasive that they are colloquially referred to as “the fifth season” in the region. During this season populations residing in this area including cities such as Lahore, Amritsar, Faisalabad, Multan, Delhi etc. are exposed to hazardous levels of air pollution. This study attempts to understand the reasons for the recent increase in the intensity and frequency of intense air pollution episodes by utilizing ground-based and satellite observations. Time-series analysis, seasonal and annual variations in PM2.5 and AOD were carried out. Satellite data of UVAAI, CO, and fire count were used to determine the fire incidences and aerosol characterization. The results indicate that during the last decade, there has been an increase in air pollution sources while crop residue burning, and motor vehicles have greatly contributed to the increased frequency and intensity of such events. The meteorological and topographical data analysis revealed that the IGP provides ample cloud-condensation nuclei and optimum conditions for the accumulations of pollutants, especially in the post-monsoon and winter periods. The Mann-Kendall test was performed to confirm the annual and seasonal trends of PM2.5. The major cities of South Asia such as Lahore, Delhi, Kathmandu, and Dhaka have recently shown a decreasing trend with respect to annual AOD. However, an increase has been observed for the period of post-monsoon especially for cities of Lahore and Delhi. To distinguish potential sources of air pollutants during extreme smog episodes in the region, Analytical Hierarchy Process (AHP) was conducted to estimate the most contributing factors to the annual smog episodes. According to the data, the transboundary pollution resulting from open-field agriculture fires has been assigned moderate importance over vehicular emissions. A HYSPLIT trajectory model coupled with ground observations and satellite data shows that the agricultural fires do have a profound impact on the air quality of the region. It highlights the importance of transboundary pollution and cooperation among cities, regions, and countries across the shared airshed of the Indo-Gangetic Plains.

Influence of Secondary Inorganic Aerosol on the Concentrations of PM2.5 and PM0.1 during Air Pollution Episodes in Hanoi, Vietnam

Influence of Secondary Inorganic Aerosol on the Concentrations of PM2.5 and PM0.1 during Air Pollution Episodes in Hanoi, Vietnam

First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact

Here we retrieve global daily 1 km gapless PM2.5 concentrations via machine learning and big data, revealing its spatiotemporal variability at an exceptionally detailed level everywhere every day from 2017 to 2022, valuable for air quality monitoring, climate change, and public health studies. We find that 96%, 82%, and 53% of Earth’s populated areas are exposed to unhealthy air for at least one day, one week, and one month in 2022, respectively. Strong disparities in exposure risks and duration are exhibited between developed and developing countries, urban and rural areas, and different parts of cities. Wave-like dramatic changes in air quality are clearly seen around the world before, during, and after the COVID-19 lockdowns, as is the mortality burden linked to fluctuating air pollution events. Encouragingly, only approximately one-third of all countries return to pre-pandemic pollution levels. Many nature-induced air pollution episodes are also revealed, such as biomass burning.

Development and Evaluation of a North America Ensemble Wildfire Air Quality Forecast: Initial Application to the 2020 Western United States “Gigafire”

AbstractWildfires emit vast amounts of aerosols and trace gases into the atmosphere, exerting myriad effects on air quality, climate, and human health. Ensemble forecasting has been proposed to reduce the large uncertainties in the wildfire air pollution forecast. This study presents the development of a multi‐model ensemble (MME) wildfire air pollution forecast over North America. The ensemble members include regional models (GMU‐CMAQ, NACC‐CMAQ, and HYSPLIT), global models (GEFS‐Aerosols, GEOS5, and NAAPS), and global ensemble (ICAP‐MME). Performance of the ensemble forecast was evaluated with MAIAC and VIIRS‐SNPP retrieved aerosol optical depth (AOD) and AirNow surface PM2.5 measurements during the 2020 Western United States “Gigafire” events (August–September 2020). Compared to individual models, the ensemble mean significantly reduced the biases and produced more consistent and reliable forecasts during extreme fire events. For AOD forecasts, the ensemble mean was able to improve model performance, such as increasing the correlation to 0.62 from 0.33 to 0.57 by individual models compared to VIIRS AOD. The ensemble mean also yields the best overall RANK (a composite indicator of four statistical metrics) when compared to VIIRS and MAIAC AOD. For the surface PM2.5 forecast, the ensemble mean outperformed individual models with the strongest correlation (0.60 vs. 0.43–0.54 by individual models), lowest fractional bias (0.54 vs. 0.55–1.32), highest hit rate (87% vs. 40%–82%), and highest RANK (2.83 vs. 2.40–2.81). Finally, the ensemble shows the potential to provide a probability forecast of air quality exceedances. The exceedance probability forecast can be further applied to early warnings of extreme air pollution episodes during large wildfire events.

Investigation of the summer 2018 European ozone air pollution episodes using novel satellite data and modelling

Abstract. In the summer of 2018, Europe experienced an intense heatwave which coincided with several persistent large-scale ozone (O3) pollution episodes. Novel satellite data of lower-tropospheric column O3 from the Global Ozone Monitoring Experiment-2 (GOME-2) and Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp satellite showed substantial enhancements in 2018 relative to other years since 2012. Surface observations also showed ozone enhancements across large regions of continental Europe in summer 2018 compared to 2017. Enhancements to surface temperature and the O3 precursor gases carbon monoxide and methanol in 2018 were co-retrieved from MetOp observations by the same scheme. This analysis was supported by the TOMCAT chemistry transport model (CTM) to investigate processes driving the observed O3 enhancements. Through several targeted sensitivity experiments we show that meteorological processes, and emissions to a secondary order, were important for controlling the elevated O3 concentrations at the surface. However, mid-tropospheric (∼ 500 hPa) O3 enhancements were dominated by meteorological processes. We find that contributions from stratospheric O3 intrusions ranged between 15 %–40 %. Analysis of back trajectories indicates that the import of O3-enriched air masses into Europe originated over the North Atlantic, substantially increasing O3 in the 500 hPa layer during summer 2018.

Response of organic aerosol to Delhi's pollution control measures over the period 2011–2018

Some of the world's highest air pollution episodes occur in Delhi, India and studies have shown particulate matter (PM) is the leading air pollutant to cause adverse health effects on Delhi's population. It is therefore vital to chart sources of PM over long time periods to effectively identify trends, particularly as multiple air quality mitigation measures have been implemented in Delhi over the past 10 years but remain unevaluated. An automated offline aerosol mass spectrometry (AMS) method has been developed which has enabled high-throughput analysis of PM filters. This novel offline-AMS method uses an organic solvent mix of acetone and water to deliver high extraction recoveries of organic aerosol (OA) (95.4 ± 8.3%). Positive matrix factorisation (PMF) source apportionment was performed on the OA fraction extracted from PM10 filter samples collected in Delhi in 2011, 2015 and 2018 to provide snapshots of the responses of OA to changes in sources in Delhi. The nine factors of OA resolved by PMF group into four primary source categories: traffic, cooking, coal-combustion and burning-related (solid fuel or open burning). Burning-related OA made the largest contribution during the winter and post-monsoon, when total OA concentrations were at their highest. Annual mean burning-related OA concentrations declined by 47% between 2015 and 2018, likely associated with the 2015 ban on open waste burning and controls and incentives to reduce crop-residue burning. Compositional analysis of OA factors shows municipal waste burning tracers still present in 2018, indicating further scope to reduce burning-related OA. The closure of the two coal power stations, along with initiatives to decrease coal use in industry, businesses, and residential homes, resulted in a significant decrease (87%) in coal-combustion OA. This corresponds to a 17% reduction in total OA, which shows the effectiveness of these measures in reducing PM10. Increases in traffic OA appear to have been offset by the introduction of the Bharat stage emissions standards for vehicles as the increases do not reflect the rapid increase in registered vehicles. However, daytime restrictions on heavy goods vehicles (HGVs) entering the city is linked to large increases in PM10 during the winter and post-monsoon, likely because the large influx of diesel-engine HGVs during the early mornings and evenings is timed with a particularly low planetary boundary layer height that enhances surface concentrations.

A mobile car monitoring system as a supplementary tool for air quality monitoring in urban and rural environments: the case study from Poland

Living in healthy environment should be regarded as a primary human right and not a privilege rendered to chosen ones. For that reason, a national air monitoring grid should be as extensive as possible. Unfortunately, small towns and villages, which are also exposed to air pollution episodes are not commonly covered by monitoring grid. Fixed air monitoring systems have their limitations, which can be overcome by e.g., properly validated, reliable but cheaper mobile monitoring systems. The aim of this study was to assess the use of a car mobile PM10 monitoring system to study ambient air quality in rural communities surrounding Kraków, not covered by fixed monitoring grid. A monitoring dataset was collected during 6 winter car campaigns conducted between December 2021 and March 2022. Except for providing multiple monitoring data, the main goal pertain to methodological aspect of the mobile system, including its validation, indicating its advantages, limitations as well as providing recommendations for the future mobile studies. Our car mobile monitoring system was useful in identifying a local hot-spots with good spatial and temporal resolution, thus giving the local government decision-making tool for taking appropriate action in places not included in national monitoring grid.