Extreme Pollution Research Articles

Heavy metals pollution in riverine sediments: Distribution, source, and environmental implications.

This research reports heavy metal pollution in riverine sediments from River Kabul, Pakistan, which could endanger human health and ecology via the food web. The results revealed a substantial special variation in the average contents (mg/kg) of chromium (Cr), manganese (Mn), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), cadmium (Cd), mercury (Hg), lead (Pb), iron (Fe), and aluminum (Al) in riverine sediments, in the order of Fe (20,234.51) > Al (17,550.86) > Mn (375.45) > Zn (149.08) > Ni (89.11) > Cr (83.36) > Pb (45.29) > Cu (19.86) > Cd (7.48) > Co (6.28) > Hg (0.81). Among the heavy metals, Cd exhibited the highest degree of pollution along the river, followed by Hg > Ni > Zn > Pb > Al > Cr > Mn > Fe > Cu > Co. The overall contamination factor (CF) values for the sum of heavy metals were highest at monitoring site S-9, followed by S-8 > S-10 > S-6 > S-5 > S-7 > S-1 > S-4 > S-12 > S-3 > S-2 > S-1 with pollution load index (PLI) > 1, whereas the geo-accumulation index (Igeo) values of Cd and Hg fluctuated between Levels 3, 4, and 6, suggesting moderate to extreme pollution in the river. The correlation statistics determined the fate and distribution of heavy metals by establishing significant positive correlations between the specific metals of bounded sediments. The cluster analysis separates the correlated metals into Groups A and B, and Groups 1 and 2. While the principal component analysis evaluates the qualitative behavior of clustering by discerning industrial, agrochemicals, mining, and domestic wastewater discharges, leakages of lubricants along with multiple geogenic inputs, erosion of mafic and ultramafic rocks, and minimal atmospheric deposition are all potential sources of Cr, Mn, Co, Ni, Cu, Zn, Cd, Hg, Pb, Fe, and Al contamination. In terms of risk, the contaminations of Mn, Co, Cu, Zn, and Pb in riverine sediments were 85, 100, 100, 17, and 11%, respectively, representing a rare biological influence because their value is less than their corresponding threshold effect concentrations (TECs), whereas the levels of Mn, Ni, Cd, and Hg were above their probable effect concentrations (PECs) of 100, 100, 81, and 52%, respectively, representing prominent adverse biological influence. Based on consensus-based TECs and PECs, the contamination levels of Cr, Mn, Zn, Cd, Hg, and Pb were 100, 85, 83, 19, 48, and 90%, respectively, indicating occasionally exhibited adverse biological effects on the riverine population. Besides, the overall potential ecological risk index (PERI) of Cd and Hg, in particular, exhibited the maximum pollution level ( ≥ 320), suggesting a very high potential ecological risk in the drainage that requires special attention from pollution control authorities.

Data Augmentation Strategies for Improved PM2.5 Forecasting Using Transformer Architectures

Breathing in fine particulate matter of diameter less than 2.5 µm (PM2.5) greatly increases an individual’s risk of cardiovascular and respiratory diseases. As climate change progresses, extreme weather events, including wildfires, are expected to increase, exacerbating air pollution. However, models often struggle to capture extreme pollution events due to the rarity of high PM2.5 levels in training datasets. To address this, we implemented cluster-based undersampling and trained Transformer models to improve extreme event prediction using various cutoff thresholds (12.1 µg/m3 and 35.5 µg/m3) and partial sampling ratios (10/90, 20/80, 30/70, 40/60, 50/50). Our results demonstrate that the 35.5 µg/m3 threshold, paired with a 20/80 partial sampling ratio, achieved the best performance, with an RMSE of 2.080, MAE of 1.386, and R2 of 0.914, particularly excelling in forecasting high PM2.5 events. Overall, models trained on augmented data significantly outperformed those trained on original data, highlighting the importance of resampling techniques in improving air quality forecasting accuracy, especially for high-pollution scenarios. These findings provide critical insights into optimizing air quality forecasting models, enabling more reliable predictions of extreme pollution events. By advancing the ability to forecast high PM2.5 levels, this study contributes to the development of more informed public health and environmental policies to mitigate the impacts of air pollution, and advanced the technology for building better air quality digital twins.

Spatio-temporal analysis of extreme air pollution and risk assessment.

Extreme air pollution poses global health and environmental threats, necessitating robust policy interventions. This study first analyses the surface mass concentration of major aerosols (such as black carbon, organic carbon, dust, sea salts, and sulphates) to estimate global PM2.5 concentrations from 1980 to 2023. The developed model-estimated PM2.5 database was validated against data from 526 cities worldwide, showing strong accuracy, with RMSE, r, and R2 values of 7.47μg/m³, 0.87, and 0.75, respectively. The motivation arises from the need to understand whether recent pollution increases are driven by rising emissions or natural variability, given the significant impacts on life and property. To assess both short-and long-term pollution trends, magnitudes, and risks, we proposed twelve novel extreme pollution indices, which comprehensively characterize the spatial and temporal variations in pollution. The highest PM2.5 concentrations were observed in regions near the Saharan Desert, reaching up to 90,000μg/m³. However, significant PM2.5TOT (total pollution) concentrations were also found in the Indo-Gangetic Plain (IGP) and eastern China, ranging from 20,000 to 40,000μg/m³. Persistent pollution burdens North Africa for approximately 350 days annually, while the IGP and eastern China experience extreme pollution for over 200 days yearly. Other pollution indices highlight the intensity and frequency of pollution in regions such as North Africa, IGP, Eastern Russia, Western USA, and Eastern China, revealing critical regional air quality challenges. Our analysis identifies cities in low-income and middle-income countries, such as New Delhi, Lahore, Dhaka, and Dammam, as being at extreme risk scores above 90 out of 100. Meanwhile, cities like Ghaziabad, Chongqing, Kolkata, Mumbai, and East London fall into the high-risk category, scoring between 60 and 80. Conversely, most cities in the EU, USA, and Canada are at very low risk, a result of the effective implementation of strategic air pollution norms and policies. The study promotes a phased approach for low- and middle-income regions, emphasizing achievable air quality standards, low-cost monitoring, targeted interventions, urban greening, public awareness, and innovative financing for improvements.

Increasing microplastic concentrations have nonlinear impacts on the physiology of reef-building corals.

The pollution of marine environments with plastics, particularly microplastic (MP, i.e., plastic particles <5mm), is a major threat to marine biota, including corals. While the effects of MPs are increasingly well understood, knowledge of how different concentrations of naturally occurring MP mixtures affect reef-building corals is still limited. Therefore, we aimed to elucidate the relationship of MP concentrations and their effects on reef-building corals. For this, we exposed two reef-building coral species (Stylophora pistillata and Pocillopora verrucosa) in a 12-week experiment to MPs at a gradient of concentrations (0, 0.1, 1, 10, and 100mg·L-1). Specifically, we examined effects on the coral host physiology (i.e., surface and volume growth, calcification, necrosis, and polyp activity), and the photosynthetic activity of the photosymbionts (i.e., effective and maximum quantum yield, maximum relative electron transport rate, minimum saturating irradiance, and light capture efficiency). To mimic natural conditions, we used a MP mixture consisting of six polymers in forms of fibers and fragments. Both coral species showed reduced growth rates, necrosis, lower polyp activity, and an upregulation of photosynthesis, which intensified with increasing MP concentrations. While the effects on the coral host mostly showed basic linear or nonlinear dose-response relationships, the effects on the photosymbionts revealed more complex nonlinear dose-response relationships, and photosynthesis was only upregulated after a species-specific threshold. We found that high and extreme pollution scenarios caused strong adverse effects on coral physiology, while current low to moderate concentrations had minor effects. Increasing concentrations had amplifying effects, likely due to the disproportionately higher frequency of entanglement, leading to more frequent direct contact and potential transfer of toxins or pathogens. These results suggest that corals can cope with current average pollution levels. However, they also highlight the need for measures to limit permanent increases of MP pollution to protect the health of coral reefs.

Improving the Forecast Accuracy of PM2.5 Using SETAR-Tree Method: Case Study in Jakarta, Indonesia

Air pollution in Jakarta, one of the most polluted cities globally, has reached critical levels, with PM2.5 concentrations exceeding the WHO guidelines and posing significant health risks. Accurate forecasting of PM2.5 is crucial for effective air quality management and public health interventions. PM2.5 exhibits significant nonlinear fluctuations; thus, this study employed two machine learning approaches: self-exciting threshold autoregressive tree (SETAR-Tree) and long short-term memory (LSTM). The SETAR-Tree model integrates regime-switching capabilities with decision tree principles to capture nonlinear patterns, while LSTM models long-term dependencies in time-series data. The results showed that: (1) SETAR-Tree outperformed LSTM, achieving lower RMSE (0.1691 in-sample, 0.2159 out-sample) and MAPE (2.83% in-sample, 2.98% out-sample) compared to LSTM’s RMSE (0.2038 in-sample, 0.2399 out-sample) and MAPE (3.48% in-sample, 4.05% out-sample); (2) SETAR-Tree demonstrated better responsiveness to sudden regime changes, capturing complex pollution patterns influenced by meteorological and anthropogenic factors; (3) PM2.5 in Jakarta often exceeds the WHO limits, highlighting this study’s importance in supporting strategic planning and providing an early warning system to reduce outdoor activity during extreme pollution.

Source and Ecological Risk Assessment of Potentially Toxic Metals in Urban Riverine Sediments Using Multivariate Analytical and Statistical Tools

Multivariate and statistical tool advancements help to assess potential pollution threats, their geochemical distribution, and the competition between natural and anthropogenic influences, particularly on sediment contamination with potentially toxic metals (PTMs). For this, riverine sediments from 25 locations along urban banksides of the River Ravi, Pakistan, were collected and analyzed to explore the distribution, pollution, ecological, and toxicity risk indices of PTMs like Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Sb, Sn, Sr, V, and Zn using Inductively Coupled Plasma–Optical Emission Spectrometry (ICP-OES) technique. Additionally, techniques such as X-ray Diffraction (XRD) and Scanning Electron Microscopy–Energy Dispersive X-ray Spectroscopy (SEM-EDS) were employed to investigate the mineralogical and morphological aspects. The results indicated that mean concentrations (mg kg−1) of Cd (2.37), Cr (128), Hg (16.6), Pb (26.6), and Sb (2.44) were significantly higher than reference values given for upper continental crust (UCC) and world soil average (WSA), posing potential threats. Furthermore, the geochemical pollution indices showed that sediments were moderately polluted with Cd (Igeo = 2.37, EF = 12.1, and CF = 7.89) and extremely polluted with Hg (Igeo = 4.54, EF = 63.2, and CF = 41.41). Ecological and toxicity risks were calculated to be extremely high, using respective models, predominantly due to Hg (Eri = 1656 and ITRI = 91.6). SEM-EDS illustrated the small extent of anthropogenic particles having predominant concentrations of Zn, Fe, Pb, and Sr. Multivariate statistical analyses revealed significant associations between the concentrations of PTMs and the sampling locations, highlighting the anthropogenic contributions linked to local land-use characteristics. The present study concludes that River Ravi sediments exhibit moderate levels of Cd and extreme pollution by Hg, both of which contribute highly to extreme ecological and toxicity risks, influenced by both natural and anthropogenic contributions.

An overview of climate changes and its effects on health - from mechanisms to One Health.

Human activities, primarily the burning of fossil fuels, widespread deforestation, soil erosion or machine-intensive farming methods, manufacturing, food processing, mining, and construction iron, cement, steel, and chemicals industry, have been the main drivers of the observed increase in Earth's average surface temperature and climate change. Rising global temperatures, extreme weather events, ecosystems disruption, agricultural impacts, water scarcity, problems in access to good quality water, food and housing, and profound environmental disruptions such as biodiversity loss and extreme pollution are expected to steeply increase the prevalence and severity of acute and chronic diseases. Its long-term effects cannot be adequately predicted or mitigated without a comprehensive understanding of the adaptive ecosystems. Studying the complex interaction between environmental aggressors and the resilient adaptive responses requires the exposomic and the One Health approaches. The problem is broad and affects the whole ecosystem, plants, pets and animals in addition to humans. The central role of the epithelial barrier, microbiome, and diet as key pillars for an adaptive tolerogenic immune response should be explored for increasing resilience at the individual level. A radical change in mindset worldwide, with sustainable solutions and adaptive strategies and climate-resilience and health equity policies at their center, should be achieved quickly through increased awareness based on solid scientific data.

A Comparative Analysis of Satellite-Derived CO Retrievals During the 2020 Wildfires in North America.

In September 2020, the Western United States experienced anomalously severe wildfires that resulted in carbon monoxide (CO) emissions almost three times the 2001-2019 average. In this study, we investigate the influence of wildfires on atmospheric carbon monoxide (CO) variability through a comparative analysis of observations from the Measurements of Pollution in the Troposphere (MOPITT), the Infrared Atmospheric Sounding Interferometer (IASI), and the Tropospheric Monitoring Instrument (TROPOMI). Our focus is on the North American domain, aiming to understand the differences among these products. In general, all instruments show excellent agreement under typical atmospheric CO conditions. However, notable discrepancies were observed in the CO data from the three sensors, particularly in regions with elevated CO total column (TC) values. IASI and TROPOMI consistently showed higher CO values over the western U.S. compared to MOPITT. During the fire episodes, we found that the IASI retrievals suggested higher CO abundances near the surface than the MOPITT thermal infrared retrievals that are probably the result of the differences in the covariance matrices used in IASI and MOPITT retrievals. We also found that the high IASI and TROPOMI CO observations over the western U.S. coincided with high values of the TROPOMI aerosol index (AI), suggesting the presence of absorbing aerosols. The analysis exhibited better agreement between TROPOMI and MOPITT CO TC when the AI values were low. Our results suggest that appropriate quality filtering should be employed when analyzing pollution events with these data. In particular, utilizing the AI for quality filtering may be useful when analyzing extreme pollution events with these satellite products.

Daily PM2.5 and Seasonal-Trend Decomposition to Identify Extreme Air Pollution Events from 2001 to 2020 for Continental Australia Using a Random Forest Model

Robust high spatiotemporal resolution daily PM2.5 exposure estimates are limited in Australia. Estimates of daily PM2.5 and the PM2.5 component from extreme pollution events (e.g., bushfires and dust storms) are needed for epidemiological studies and health burden assessments attributable to these events. We sought to: (1) estimate daily PM2.5 at a 5 km × 5 km spatial resolution across the Australian continent between 1 January 2001 and 30 June 2020 using a Random Forest (RF) algorithm, and (2) implement a seasonal-trend decomposition using loess (STL) methodology combined with selected statistical flags to identify extreme events and estimate the extreme pollution PM2.5 component. We developed an RF model that achieved an out-of-bag R-squared of 71.5% and a root-mean-square error (RMSE) of 4.5 µg/m3. We predicted daily PM2.5 across Australia, adequately capturing spatial and temporal variations. We showed how the STL method in combination with statistical flags can identify and quantify PM2.5 attributable to extreme pollution events in different locations across the country.

Deciphering decadal urban ozone trends from historical records since 1980.

Ozone pollution is a major environmental threat to human health. Timely assessment of ozone trends is crucial for informing environmental policy. Here we show that for the most recent decade (2013-2022) in the northern hemisphere, warm-season (April-September) mean daily 8-h average maximum ozone increases much faster in urban regions with top ozone levels (mainly in the North China Plain, 1.2±1.3 ppbv year-1) than in other, low-ozone regions (0.2±0.9 ppbv year-1). These trends widen the ozone differences across urban regions, and increase extreme pollution levels and health threats from a global perspective. Comparison of historical trends in different urban regions reveals that ozone increases in China during 2013-2022 differ in magnitude and mechanisms to historical periods in other regions since 1980. This reflects a unique chemical environment characterized by exceptionally high nitrogen oxides and aerosol concentrations, where reducing ozone precursor emissions leads to substantial ozone increase. Ozone increase in China has slowed down in 2018-2022 compared to 2013-2017, driven by ongoing emission reductions, but with ozone-favorable weather conditions. Historical ozone evolution in Japan and South Korea indicates that ozone increases should be suppressed with continuous emission reduction. Increasing temperature and associated wildfires have also reversed ozone decreases in the USA and Europe, with anthropogenic ozone control slowing down in recent decades.

Heavy metals in homestead soil: Metal fraction contents, bioaccessibility, and risk assessment

Rapid urbanization in China has led to the disappearance of countless villages and the transformation of homestead land into cultivated land or grassland. The quality of homestead soil (HS) plays a pivotal role in land-use conversion and reuse strategies, so the current state of heavy metal pollution in HS deserves attention. This study determined the fraction contents, bioaccessibility, risks, and affecting factors of Hg, As, Cd, Pb, Cu, and Zn in HS by comparing them with soil in cultivated land (CS), grassland (GS), homestead-converted cultivated land (HCS), and homestead-converted grassland (HGS). Results demonstrate that the contents of the six metals exceed background values, especially for Cd and Hg, resulting in significant pollution and elevated ecological risk. Distinct from the dominant residual fraction of other metals, the extractable fraction of Cd shows the highest proportion, which also contributes most to the high values of the Risk Assessment Code and extreme pollution conditions in HS, GS, and CS. Moreover, pH shows predominantly negative relations with the effective available and potentially available contents, while the effects of organic carbon fractions are notably the opposite. Furthermore, CS and GS suggest higher non-carcinogenic and carcinogenic risks than in the converted soil. This study indicates that HS has a lower metal accumulation risk compared with cultivated land and grassland, and homestead conversion seems to restrict the bioaccessibility of metals in soil.

Biomass-burning sources control ambient particulate matter, but traffic and industrial sources control volatile organic compound (VOC) emissions and secondary-pollutant formation during extreme pollution events in Delhi

Abstract. Volatile organic compounds (VOCs) and particulate matter (PM) are major constituents of smog. Delhi experiences severe smog during the post-monsoon season, but a quantitative understanding of VOCs and PM sources is still lacking. Here, we conduct a source apportionment study for VOCs and PM using a recent (2022), high-quality dataset of 111 VOCs, PM2.5, and PM10 in a positive matrix factorization (PMF) model. Contrasts between clean monsoon air and polluted post-monsoon air, VOC source fingerprints, and molecular tracers enabled us to differentiate paddy residue burning from other biomass-burning sources, which had previously been impossible. Burning of fresh paddy residue, as well as residential heating and waste burning, contributed the most to observed PM10 levels (25 % and 23 %, respectively) and PM2.5 levels (23 % and 24 %, respectively), followed by heavy-duty vehicles fuelled by compressed natural gas (CNG), with a PM10 contribution of 15 % and a PM2.5 contribution of 11 %. For ambient VOCs, ozone formation potential, and secondary-organic-aerosol (SOA) formation potential, the top sources were petrol four-wheelers (20 %, 25 %, and 30 %, respectively), petrol two-wheelers (14 %, 12 %, and 20 %, respectively), industrial emissions (12 %, 14 %, and 15 %, respectively), solid-fuel-based cooking (10 %, 10 %, and 8 %, respectively), and road construction (8 %, 6 %, and 9 %, respectively). Emission inventories tended to overestimate residential biofuel emissions at least by a factor of 2 relative to the PMF output. The major source of PM pollution was regional biomass burning, while traffic and industries governed VOC emissions and secondary-pollutant formation. Our novel source apportionment method even quantitatively resolved similar biomass and fossil fuel sources, offering insights into both VOC and PM sources affecting extreme pollution events. This approach represents a notable advancement compared to current source apportionment approaches, and it could be of great relevance for future studies in other polluted cities and regions of the world with complex source mixtures.

Migration and natural attenuation of leachate pollutants in bedrock fissure aquifer at a valley landfill site

Groundwater pollution from valley type landfills is concerning, and natural attenuation by contaminants is increasingly relied upon. However, the reliability of natural attenuation in such complex sites has been called into question due to incomplete understanding of their attenuation mechanisms. Therefore, we conducted field investigations, monitoring analyses, mathematical statistics, and machine learning techniques to elucidate the natural attenuation mechanisms of pollutants within bedrock fissures at a prototypical valley type landfill located in the east Yanshan Mountains, China. Our results indicate that 50% of the monitored indicators showed extreme pollution in bedrock fissure aquifers, due to seepage from the valley type landfill site. Ammonia nitrogen, arsenic, cadmium, lead, iron, manganese, and mercury were among the contaminants that could pose serious risks to human health. Pollutant concentrations in bedrock fissure aquifers were lower during the rainy season compared to the dry season as the aquifer was rapidly recharged by strong rainfall runoff. The initial concentration of bedrock fissure water generally increased during the flow through the landfill. However, significant natural attenuation of total dissolved solids, oxygen consumption, ammonia, cadmium, and lead occurred after passing through the landfill (p < 0.05), with attenuation coefficients of 0.0041 m-1, 2.56 × E−5m−2, 4.18 × E−5m−2、0.0015 m-0.99, and 6.83 × E−33 m−12.49, respectively. The driving mechanisms for natural attenuation include physical migration, leaching, microbiological degradation, and adsorption, primarily occurring within 600–650 m downstream of the landfill boundary. This study makes fundamental contribution to the understanding of the migration and natural attenuation process of leachate pollutants in bedrock fissure aquifer, which will provide a scientific basis for implementation of natural attenuation strategies in complex site remediation. Future research should examine more precise evidence of natural attenuation feasibility in complex sites in conjunction with monitoring networks.

Impact of Domestic Heating on Air Pollution—Extreme Pollution Events in Serbia

Impact of Domestic Heating on Air Pollution—Extreme Pollution Events in Serbia

Features of extreme pollution and its influencing factors: evidence from China.

Extreme pollution has become a significant environmental problem in China in recent years, which is hazardous to human health and daily life. Noticing the importance of investigating the causes of extreme pollution, this paper classifies cities across China into eight categories (four groups plus two scenarios) based on the generalized extreme value (GEV) distribution using hourly station-level concentration data, and a series of multi-choice models are employed to assess the probabilities that cities fall into different categories. Various factors such as precursor pollutants and socio-economic factors are considered after controlling for meteorological conditions in each model. It turns out that concentration, concentration, and population density are the top three factors contributing most to the log ratios. Moreover, in both left- and right-skewed cases, the influence of a one-unit increase of concentration on the relative probability of cities falling into different groups shows an increasing trend, while those of concentration show a decreasing trend. At the same time, the higher the extreme pollution level, the bigger the effect of and concentrations on the probability of cities falling into normalized scenarios. The multivariate logit model is used for prediction and policy simulations. In summary, by analyzing the influences of various factors and the heterogeneity of their influence patterns, this paper provides valuable insights in formulating effective emission reduction policies.

Distribution, source apportionment and ecological risk assessment of heavy metals in Limbe River sediments, Atlantic Coast, Cameroon Volcanic Line

The objective of this research was to assess sediments contamination, provenance and metal pollution using single and complex pollution indicators. Limbe River sediment samples were analysed using Inductively Coupled Plasma—Mass Spectrometry. The Limbe River sediments exhibit high content in Fe2O3 sourced from haematization of basaltic rocks. The concentration of Cd, Co, Cr, Mn, Ni, Pb, Sn, Sr, V and Zn exceed upper continental crust and local baseline values for heavy metals. Correlation matrix analysis indicates the presence of common input sources and similar geochemical characteristics of heavy metals. Contamination factor (CF: 0.01–10.34), contamination degree (Cdeg: 14.91–36.57), pollution load index (PLI: 0.84–1.77), and nemerow integrated pollution index (NIPI: 0.2–1.7) indicates significant to high levels of contamination. The enrichment factor (EF: 0–5.67) and geo-accumulation index (Igeo: -7.55–2.59), suggests moderate to significant metal pollution of the Limbe River ecosystem, suggesting potential ecological effects. Modified hazard quotient (mHQ: 0.082–6.35) and toxic risk index (TRI: 10.49–27.43), indicates low to extreme pollution severity and high toxicity risks of heavy metals to the aquatic ecosystem fauna and flora. Primarily anthropogenic activities and secondary lithogenic provenance is attributed to sediments contamination and metals pollution. This study suggests that a greater emphasis should be placed on monitoring various provenances of metals entering the aquatic ecosystem from anthropogenic practices. The study emphasizes the significance of continual protection of aquatic ecosystem and biological resources and adaptive management. The development of reasonable goals for monitoring and remedial programmes can be aided by this data.

Long-term trends and probability distribution functions of air pollutant concentrations in the megacity of São Paulo

Air quality conditions in many urban areas have improved in the last decades as a consequence of air pollution control policies and regulations. Mitigation strategies have been successful in reducing the concentration of primary pollutants like inhalable particulate matter (PM10), but the control of secondary pollutants like tropospheric ozone (O3) is still challenging in megacities like the Metropolitan Area of São Paulo (MASP) in Brazil. To support the development of effective mitigation strategies, it is crucial to characterize the statistical behavior of air pollutant concentrations and its long-term evolution. Probability Density Functions (PDF) can be useful to model site-specific air quality conditions, providing estimates for the frequency of extreme pollution events and exceedance of air quality standards. The current study aims to characterize which PDF model better fits and characterizes the variability of PM10 and O3 concentrations in the MASP. For that, daily maximum moving average concentrations were analyzed between 2000 and 2023, characterizing the long-term trends and the frequency of exceedance of air quality standards. PM10 concentrations followed a lognormal PDF, with an expected value of 31 ± 15 µg.m-3. O3 followed a Gamma PDF, with an expected value of 68 ± 25 µg.m-3. A consistent long-term decrease was observed for PM10 (-1.04 ± 0.09 µg.m-3.yr-1), while O3 showed an increasing trend of 0.51 ± 0.04 µg.m-3.yr-1 in the summer. In recent years (2021-2023), the probability of exceedance of the World Health Organization guideline was 17.4 and 11.0%, respectively, for PM10 and O3. In 2020, a statistically significant increase in O3 expected values was observed, possibly associated with changes in the emission patterns of precursors due to the mobility restrictions imposed by the COVID-19 pandemic.

Evaluation of potentially toxic metals in tailings from Busia gold mine fields of eastern Uganda

Busia Gold mines in eastern Uganda generate significant wastes that contribute to environmental and health challenges in the region. This study assessed the extent of 9 potentially toxic metals (PTMs) present in Busia gold mine tailings. The study also evaluated environmental risks for each of the PTMs, using contamination indices. The indices were: index of geo accumulation (Igeo); potential ecological risk index (RI); contamination factor (CF); pollution load index (PLI) and ecological risk factor (ERI). Tailing samples were collected at two distinct depths; surface (0–30 cm) and deep (30–60 cm) from four sites. The samples were analyzed using an Atomic Absorption Spectrometry (AAS). The Igeo results showed that As and Hg recorded extreme pollution levels (Igeo >5) in all 4 sites. The other seven PTMs in all the sites were classified between heavily polluted to unpolluted categories. CF results revealed extreme contamination for Hg at site-A and As at site-M with moderate contamination of Ni, Zn, and Co at sites-C and B. RI findings showed that all the sites recorded RI > 600, with the highest (RI = 80, 407) at site-A, followed by site-C (RI = 74, 536). All the sites had a high level of ecological impact with greater potential for environmental risks in the area. In the case of PLI, all the sites were classified as polluted sites (PLI > 1). Sites-M and C recorded the highest (PLI = 12), followed by sites-B and A, with PLIs of 11 and 9, respectively. ERI findings for all the sites showed significant Hg, As, and Cd contamination, (ERI >320). Site-A recorded the highest Hg contamination (ERI = 78, 825). Site-M recorded elevated levels of As and Cd, with ERI of 1, 323, and 399, respectively. Pb at site-M posed considerable potential ecological risk with an ERI of 108, while Zn, Cu, Co, Ni, and Cr presented low ecological risk across all sites with ERI˂ 40. There is, therefore, need to properly manage tailings in Busia gold fields to minimize ecological contamination to avoid impacts on human, animal, and plant health.

Impacts of synoptic weather patterns on Hefei's ozone in warm season and analysis of transport pathways during extreme pollution events

Extreme ozone pollution events (EOPEs) are associated with synoptic weather patterns (SWPs) and pose severe health and ecological risks. However, a systematic investigation of the meteorological causes, transport pathways, and source contributions to historical EOPEs is still lacking. In this paper, the K-means clustering method is applied to identify six dominant SWPs during the warm season in the Yangtze River Delta (YRD) region from 2016 to 2022. It provides an integrated analysis of the meteorological factors affecting ozone pollution in Hefei under different SWPs. Using the WRF-FLEXPART model, the transport pathways (TPPs) and geographical sources of the near-surface air masses in Hefei during EOPEs are investigated. The results reveal that Hefei experienced the highest ozone concentration (134.77 ± 42.82 µg/m³), exceedance frequency (46 days (23.23 %)), and proportion of EOPEs (21 instances, 47.7 %) under the control of peripheral subsidence of typhoon (Type 5). Regional southeast winds correlated with the ozone pollution in Hefei. During EOPEs, a high boundary layer height, solar radiation, and temperature; low humidity and cloud cover; and pronounced subsidence airflow occurred over Hefei and the broader YRD region. The East-South (E_S) patterns exhibited the highest frequency (28 instances, 65.11 %). Regarding the TPPs and geographical sources of the near-surface air masses during historical EOPEs. The YRD was the main source for land-originating air masses under E_S patterns (50.28 %), with Hefei, southern Anhui, southern Jiangsu, and northern Zhejiang being key contributors. These findings can help improve ozone pollution early warning and control mechanisms at urban and regional scales.

Heavy metal toxicity and its human health assessment: A preliminary study from the Perumal Lake sediments, Tamil Nadu, India

The current investigation manifests the heavy metal toxicity and its human health assessment to appraise the environmental deterioration of the sediments within the Perumal Lake, Tamil Nadu, India. Five surface samples were collected from the Perumal Lake in 2023, which undergoes the selective perception of the granulometric analysis, implying supremacy of the clay content and limited sand and silt contents. The organic matter indicates the higher input of waste disposal, and the CaCO3 illustrates the existence of shell fragments in the lake environments. Noteworthy results of the heavy metal concentration as arranged in the devaluation order as Zn &gt; Cu &gt; Fe &gt; Cr &gt; Ni &gt; Mn &gt; Pb &gt; Co &gt; Cd. Followed by the heavy metal concentration, the environmental contamination indices such as Igeo denote that except for Cd metal, other heavy metals indicate moderate to extreme pollution status. The Enrichment Factor (Ef) illustrates Fe, Co, Cd, Zn, Cu, Ni, and Pb, highlighting no metal enrichment. In contrast, Mn and Cr reveal low metal enrichments, and the Cf reveals all the heavy metals, which argues for low to high contamination ranges. The Cd and the mCd are categorized as very high, and PERI underscores the low-risk category. The Hazard Index (HI) of the non-carcinogenic category and carcinogenic demonstrates that children and adults are primarily at risk of ingestion. At the same time, the dermal pathway indicates low jeopardy to children and adults and no risk to humans. The Heavy Metal Toxicity Load (HMTL) poses the sample location three registers a significant accumulation of toxic contagion proposed to remove from the sediments. Realm of heavy metal toxicity and its human health assessment underscores for researchers in environmental quality determination, and the strategies discussed such as phytoremediation, in-situ capping, and biotechnological techniques, may be helpful in evaluation and implement remediation methods of pollution in aquatic environments.