Pollution Levels Research Articles

Trends of air pollution variations during pre-Diwali, Diwali and post-Diwali periods and health risk assessment using HAQI in India

During 2019 to 2022, a comprehensive study was conducted across 71 stations spanning over 15 states in India to investigate air quality variations during the pre-Diwali, Diwali, and post-Diwali periods. The investigation, centred on the extensive use of firecrackers during festive times, revealed a short-term deterioration in air quality with long-lasting impacts. PM2.5 and PM10 levels were found to significantly increase during Diwali and the post-Diwali period compared to the pre-Diwali period. The levels of PM2.5 and PM10 experience respective increases of 44.66% and 11.03%, 29.12% and 05.41%, 52.63% and 23.38%, and 39.65% and 17.61% during Diwali period from 2019 to 2022, where 68.84% and 34.32%, 44.42% and 29.42%, and 48.70% and 25.22% in 2019, 2021 and 2022 in post-Diwali period compared to the pre-Diwali. The Mann–Kendall and Sen's slope analyses reveal statistically significant positive trends (increasing) in both PM10 and PM2.5 levels during the Diwali festival period. Notably, the study introduced the Health Hazard-based Air Quality Index (HAQI), which demonstrated higher values than the National Ambient Air Quality Index (NAAQI). Unlike NAAQI, which considers individual pollutant sub-index values, and considered highest one of sub-index values in AQI analysis. HAQI provides a more comprehensive assessment of health risks associated with exposure to multiple pollutants effects. In 2022, AQI and HAQI values during pre-Diwali were 114 and 146.18, during Diwali were 173 and 332.18, and post-Diwali were 191 and 414.14, respectively. These findings underscore the elevated risk during Diwali and the post-Diwali period due to heightened pollution levels, emphasizing the need for public awareness and potential mitigation strategies. The study calls attention to the importance of considering cumulative health impacts and suggests the adoption of HAQI for more informative air quality assessments.

Statistical analysis of water quality change by total maximum daily load policy stage

This study aimed to derive major pollutants for standard watersheds with relatively high pollution levels and identify their trends. Hence, the water quality index (WQI) and multivariate statistical techniques were used to analyze the water quality evaluation of standard watershed during the total maximum daily load (TMDL) policy period implemented since 2004. The WQI was calculated for 41 standard watersheds, divided into 14 main streams and 27 tributaries. Consequently, in the main stream, WQI decreased as it moved downstream after the MS5 site. In the tributary, WQI values were found to be low at TS11, TS15, TS16, and TS17 sites. However, the Mann–Kendall test results indicated that the overall WQI value was increasing, suggesting that the water quality of the study basin was improving as the TMDL policy phase progressed. Principal component analysis of the group with relatively low WQI among the standard watersheds revealed that water temperature, dissolved oxygen, total phosphorus, chemical oxygen demand, biochemical oxygen demand, and electrical conductivity were the main variables in the main stream cluster. In the tributary stream cluster, total phosphorus, dissolved oxygen, total suspended solids, total nitrogen, and electrical conductivity showed high loading values. This study proposed a series of methods to provide the basic data required for watershed management through detailed water quality assessment methods, statistical techniques for deriving major pollutants, and trend analysis of water quality evaluation.

Characteristics of plankton community structure and applicability of biomanipulation in midstream of the Jialing River—the biggest tributary of the Yangtze River

Plankton, as an important part of the food chain in the river ecosystem, is sensitive to changes in the environment. To investigate the influence of plankton community structure on the aquatic ecosystem and further verify the applicability of biomanipulation, the midstream of the Jialing River was selected as the study area. A total of 149 species (varieties) from 7 phyla, 18 orders, 34 families, and 72 genera were identified for phytoplankton. A total of 48 species were found for zooplankton. The mean density of phytoplankton was 2.09 × 106 cells/L, and the mean density of zooplankton was 27.1 ind./L. Moreover, the mean values of the Shannon–Wiener diversity index (H′), Pielou homogeneity index (J′), and Margalef abundance index (D) for phytoplankton were 3.17, 0.76, and 4.47, respectively. The mean values of the Shannon–Wiener diversity index (H′), Pielou homogeneity index (J′), and Margalef abundance index (D) for zooplankton were 2.64, 0.92, and 5.07, respectively. Based on the diversity indices, the water quality was evaluated as oligo pollution to β-medium pollution level. Redundancy analysis (RDA) was used to explore the relationship between dominant plankton species and environmental factors. The RDA results show that the main environmental factors affecting the community structure of phytoplankton and zooplankton were water temperature (WT), electrical conductivity (EC), pH, total phosphorus (TP), and transparency (Secchi depth; SD). There were 17 functional groups in phytoplankton, which are X1, S1, Y, Y2, and so on. In zooplankton, there were six functional groups, which are Pmsl, Prsu, Pfif, Pmip, Lrsf and Lmic. Many articles have demonstrated the feasibility of using biological manipulation to control algae. The number of piscivorous fish in the midstream of the Jialing River had increased, and phytoplankton density in this survey was significantly decreased. The results show that traditional biomanipulation can be applied to this region, which can provide better scientific support for the future management of the aquatic environment in the Jialing River basin.

Mathematical Assessment of Pollution Factors from Eddy Currents in the Durrës Seaport

Eddy currents, a significant phenomenon in electromagnetic fields, were first distinguished from general electric currents by Foucault. These currents, influenced by the magnetic field vector (H), magnetic flux density (B), and electric field (E), are present in various environments, including homogeneous and non-homogeneous areas, linear and non-linear environments, and open boundary problems. In marine environments, these currents, referred to as planetary currents, contribute to electrical pollution, impacting maritime infrastructure, machinery, and ecosystems. This study aims to assess the impact of eddy currents in the Durrës Seaport, Albania, focusing on how meteorological factors such as temperature, atmospheric pressure, and humidity influence pollution levels. Using the TES-92 Electro-smog meter and the PCE-THB 40 Thermo-hygrometer and barometer, we measured electromagnetic field characteristics and meteorological parameters. Our results highlight the significant impact of stray currents on marine environments, emphasizing the need for effective measures to minimize their effects and improve the quality and selection of materials and equipment in port facilities. This study provides valuable insights into the environmental and infrastructural challenges posed by electromagnetic pollution and proposes recommendations for mitigating these impacts.

Evaluation of ecological and health risks of trace elements in soils of olive orchards and apportionment of their sources using the APCS-MLR receptor model

İzmir, Turkey’s third most populous city, is in an important position in terms of both agriculture and industry. The province, which contributes 9.3% to the country’s industrial production, also has an important potential in terms of olive cultivation. However, until now, no research has been undertaken to analyze the content of trace elements (TEs) in the soil of olive orchards in İzmir. This study was carried out to determine the pollution level and ecological risks of TEs in the olive orchards soils of İzmir province, to reveal their potential sources and to evaluate their health risks. Among the TEs, the average content of only Ni (37.9 mg/kg) exceeded the world soil average content (29 mg/kg), while the average content of only Cd (0.176 mg/kg) exceeded the upper continental crust content (0.09 mg/kg). Enrichment factor revealed that there was significant enrichment for Cd in 73.6%, Ni in 11.6% and Cr in 5.4% of olive orchards, respectively, due to polluted irrigation water and agrochemicals. Similarly, ecological risk factor indicated that there were moderate and considerable ecological risks for Cd in 48.8% and 23.3% of olive orchards, respectively. Absolute principal component scores-multiple linear regression (APCS-MLR) model showed that Ni and Cr in the study area are affected by agricultural sources, Al, Co, Cu, Fe, Mn, Pb and Zn originate from lithogenic sources, and Cd originates from mixed sources. Based on health risk evaluation methods, non-carcinogenic and carcinogenic effects would not be expected for residents. This study provides significant knowledge for evaluating soil TE pollution in olive orchards and serves a model for source apportionment and human health risk evaluation of TEs in other agricultural regions.

Assessment of groundwater fluoride and human health effects in a hard rock province of south India: Implications from Pollution Index Model (PIM) and GeographicalInformationSystem (GIS) techniques.

This research examines whetherthe groundwater in the Sivakasi Region of South India is suitable for consumption, and assesses the possible health hazards for various age demographics including infants, children, teenagers, and adults. A totalof 77 groundwater samples were gathered, covering a total area of 580km2 and analyzed for major and minor ions. The hydrogen ion concentration (pH) of the samples indicates neutral to marginally alkaline. The total dissolved solids (TDS) fluctuate from 255 to 2701mg/l and electrical conductivity varies from 364 to 3540µS/cm. A wide range of fluoride concentration was detected (0.1 to 3.2mg/l) with nearly 38% groundwater samples surpassing the proposed limit (1.5 mg/l) suggestedby the World Health Organization in 2017. Gibbs plot analysis suggested that most of thesamples were influenced by geogenic factors, primarily rock weathering in this region. Correlation analysis showed that most of the samples were impacted by both natural and human sources. The pollution index of groundwater (PIG) fluctuated from 0.67 to 2.60 with approximately 30% and 53% of samples falling into insignificant and low pollution categories, respectively. Furthermore, 10% and 5% of total samples were characterized as moderate and high pollution levels, and 2% asvery high pollution category. Spatial analysis using GIS revealed that 440.63km2 were within safe fluoride levels according to theWHO standards, while 139.32km2 were identified as risk zone. The principal component analysis (PCA1) showedstrong positive loadings on EC (0.994), TDS (0.905), Mg2+ (0.910), Cl- (0.903) and HCO3- (0.923) indicating rock water interaction. PCA2 accounts the high positive factor loading on HCO3- (0.864) indicating ion exchange and mineral leaching. The PCA1 and PCA2 indicated that variables such as mineral leaching and rock water interaction are themajor mechanisms contributing to the chemical signatures in groundwater, which may support for theelevated fluoride levels in certain areas. Risk assessments, including Hazard Quotient results showed that 71%, 61% 38%, and 34% of groundwater samples exceededthe permissible THI limit (THI > 1) for infants, children, teenagers, and adults, respectively. The study recommends implementing measures such as denitrification, defluorination, rainwater harvesting, and improved sanitation infrastructure to enhance the health conditions in the study region. Additionally, it suggests introducing educational programs in rural areas to create awarenessabout the health dangers due to consumptionof water with high fluoride levels.

The Ecological Economics of Light Pollution: Impacts on Ecosystem Service Value

Light pollution has detrimental impacts on wildlife, human health, and ecosystem functions and services. This paper explores the impact of light pollution on the value of ecosystem services. We use the Simplified All-Sky Light Pollution Ratio (sALR) as a proxy for the negative impact of light pollution and the Copernicus PROBA-V Global Landcover Database as our proxy of ecosystem service value based on previously published ecosystem service values associated with a variety of landcovers. We use the sALR value to ‘degrade’ the value of ecosystem services. This results in a 40% reduction in ecosystem service value in those areas of the world with maximum levels of light pollution. Using this methodology, the estimate of the annual loss of ecosystem service value due to light pollution is USD 3.4 trillion. This represents roughly 3% of the total global value of ecosystem services and 3% of the global GDP, estimated at roughly USD 100 trillion in 2022. A summary of how these losses are distributed amongst the world’s countries and landcovers is also presented.

Air Pollution Increases Risk of Occurrence of Intracerebral Haemorrhage but Not of Subarachnoid Haemorrhage: Time-Series Cross-Sectional Study

(1) Background: Haemorrhagic strokes (HS), including intracerebral (ICH) and subarachnoid haemorrhages (SAH), account for approximately 10–15% of strokes worldwide but are associated with worse functional outcomes and higher rates of mortality, and financial burden than ischemic stroke. There is evidence that confirmed poor air quality may increase the incidence of haemorrhagic strokes. The aim of our study was to evaluate the association between individual ambient air pollutants and the risk of haemorrhagic stroke in an urban environment without high levels of air pollution. (2) Methods: A time-series cross-sectional study design was used. A daily air pollution concentration (Agency of Regional Air Quality Monitoring in the Gdansk Metropolitan Area) and incidence of haemorrhagic strokes (National Health Fund) were obtained and covered the time period from 1 January 2014 to 31 December 2018. A generalised additive model with Poisson regression was used to estimate the associations between 24-h mean concentrations of SO2, NO, NO2, NOx, CO, PM10, PM2.5, and O3 and a daily number of haemorrhagic strokes. (3) Results: The single-day lag model results showed that NO2, NO and NOx exposure was associated with increased risk of ICH (88% events) with RR of 1.059 (95% CI: 1.015–1.105 for lag0), 1.033 (95% CI: 1.007–1.060 for lag0) and 1.031 (95% CI: 1.005–1.056 for lag0), but not for SAH (12% events). Exposure to CO was related to a substantial and statistically significant increase in incidence for 1.031 (95% CI: 1.002–1.061 for lag0) but not for SAH. Higher SO2, PM10, PM2.5, and O3 exposures were not significantly related to both ISC and SAH. (4) Conclusions: In this time-series cross-sectional study, we found strong evidence that supports the hypothesis that transient elevations in ambient NO2, NO and CO are associated with a higher relative risk of intracerebral but not subarachnoid haemorrhage.

Evaluation of WRF-Chem air quality forecasts during the AEROMMA and STAQS 2023 field campaigns

ABSTRACT A real-time air quality forecasting system was developed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to provide support for flight planning activities during the NOAA Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and NASA Synergistic TEMPO Air Quality Science (STAQS) 2023 field campaigns. The forecasting system operated on two separate domains centered on Chicago, IL, and New York City, NY, and provided 72-hour predictions of atmospheric composition, aerosols, and clouds. This study evaluates the Chicago-centered forecasting system’s 1-, 2-, and 3-day ozone (O3) forecast skill for Chiwaukee Prairie, WI, a rural area downwind of Chicago that often experiences high levels of O3 pollution. Comparisons to vertical O3 profiles collected by a Tropospheric Ozone Lidar Network (TOLNet) instrument revealed that forecast skill decreases as forecast lead time increases. When compared to surface measurements, the forecasting system tended to underestimate O3 concentrations on high O3 days and overestimate on low O3 days at Chiwaukee Prairie regardless of forecast lead time. Using July 25, 2023, as a case study, analyses show that the forecasts underestimated peak O3 levels at Chiwaukee Prairie during this regionwide bad air quality day. Wind speed and direction data indicates that this underestimation can partially be attributed to lake breeze simulation errors. Surface fine particulate matter (PM2.5) measurements, Geostationary Operational Environmental Satellite-16 (GOES-16) aerosol optical depth (AOD) data, and back trajectories from the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model show that transported Canadian wildfire smoke impacted the Lake Michigan region on this day. Errors in the forecasted chemical composition and transport of the smoke plumes also contributed to underpredictions of O3 levels at Chiwaukee Prairie on July 25, 2023. The results of this work help identify improvements that can be made for future iterations of the WRF-Chem forecasting system. Implications: Air quality forecasting is an important tool that can be used to inform the public about upcoming high pollution days so that individuals may plan accordingly to limit their exposure to health-damaging air pollutants. Forecasting also helps scientists make decisions about where to make observations during air quality field campaigns. A variety of observational datasets were used to evaluate the accuracy of an air quality forecasting system that was developed for NOAA and NASA field campaigns that occurred in the summer of 2023. These evaluations inform areas of improvement for future development of this air quality forecasting system.

Total airway mechanics and fractional exhaled nitric oxide levels of children living in banjihas (semi-basements).

The ISAAC phase III study in Korea found a higher incidence of wheezing illnesses among residents in basements or semi-basements. This study investigates the link between living in banjihas (semi-basements) and airway resistance and Th2 airway inflammation in Korean children, compared to those on higher floors. We assessed 575 fifth- and sixth-grade students (aged 10-12) in an inner-city area of South Korea. The study utilized impulse oscillometry to measure small and total airway resistance (Rrs20-5 and Rrs0, respectively) and Fractional Exhaled Nitric Oxide (FeNO) measurements to evaluate airway inflammation. We also considered a range of biological and environmental factors, including allergen sensitization, serum 25-hydroxyvitamin D levels, and urinary metabolites like VOCs, bisphenol, and triclosan. Participants were categorized by living floors: banjihas, first-fifth floors, and sixth floors or higher. Twenty-five children (4.3%) lived in banjihas, 311 (54.1%) on the first to fifth floor, and 239 (41.6%) on the sixth floor or above. Despite similar levels of allergen sensitization and urinary pollutant metabolite levels across all groups, banjiha dwellers showed significantly higher total airway resistance (adjusted &1: 0.633, 95%CI: 0.156, 1.109; P = 0.009) and a greater prevalence of elevated FeNO levels (> 35 ppb) (P = 0.033). These findings persisted after adjusting for critical factors like height, gender, BMI z-score, and birth conditions. Children in banjihas exhibit elevated airway resistance and FeNO levels independently of allergen sensitization or pollution exposure, underscoring the necessity for enhanced focus on their respiratory health in such living conditions.

Air Pollution Monitoring and Prediction using Machine Learning Algorithms

Air pollution is an increasing environmental concern worldwide, with hazardous impacts on the environment and human health. Monitoring and predicting air pollution levels accurately are crucial for implementing and mitigating its adverse effects. The purpose of this work is to forecast the value of the Air Quality Index using data preprocessing, feature extraction and selection, and machine learning based prediction techniques. The historical air quality data was collected and analysed from the dataset that included environmental data across multiple regions of India. Linear Regression, Decision Tree, Random Forest, XGBoost, RANSAC Regression, AdaBoost and LightGBM were used to process and forecast pollution concentrations. The Random Forest and LightGBM model had the highest prediction accuracy for Air Quality Index. Key Words: Air pollution, environment and human health, Air Quality Index, Machine Learning, comparative analysis, ML models, prediction, historical data, India, Regression

Ambient air pollution decreased normal fertilization rate via the mediation of seminal prosaposin

ObjectiveThis study focuses on the association between seminal concentration of prosaposin and ambient air pollutants and whether the association affects the normal fertilization rate in vitro fertilization (IVF) treatment. MethodsThe cohort of 323 couple participants aged 22–46 was recruited from Jan. 2013 to Jun. 2018. At enrollment, resident address information was obtained and semen parameters of male counterparts were evaluated according to WHO criteria. We used inverse distance weighting interpolation to estimate the levels of ambient pollutants (SO2, O3, CO, NO2, PM2.5, and PM10) in the surrounding area. The exposure of each participant was estimated based on the data gathered from air quality monitoring stations and their home address over various periods (0–9, 10–14, and 0–90 days) before semen sampling. The generalized linear regression model (GLM) and the Bayesian kernel machine regression (BKMR) were used to analyze the associations between pollutants, semen parameters, prosaposin, and normal fertilization. Additionally, the mediating effect of prosaposin and semen parameters on the link between pollutants and normal fertilization was investigated. ResultsGLM and BKMR showed exposure to ambient air pollutants was all associated with the concentration of seminal prosaposin, among them, O3 and CO were also associated with normal fertilization (-0.10, 95 %CI: −0.13, −0.06; −26.43, 95 %CI: −33.79, −19.07). Among the semen parameters, only the concentration of prosaposin and total motile sperm count (TMC) was associated with normal fertilization (0.059, 95 %CI: 0.047, 0.071; 0.016, 95 %CI: 0.012, 0.020). Mediation analysis showed that prosaposin played a stronger mediating role than TMC in the relationship between short-term exposure to O3 and fertilization (66.83 %, P<0.001 versus 3.05 %, P>0.05). ConclusionSeminal plasma prosaposin showed a stronger meditating effect reflect the correlation between ambient air pollutants and normal fertilization rate than conventional semen parameters, which may be used as one of the indicators between pollution and fertilization in IVF.

Soil urease functional stability to Hg pollution: An ecotoxicological perspective.

Mercury (Hg) is a persistent soil pollutant, and its toxicity can be evaluated using soil enzyme indicators. However, a thorough understanding of how the enzyme resists and remains resilient to Hg stress is essential, as it significantly impacts the accuracy of toxicity assessments. Therefore, it is worthwhile to understand the functional stability of urease in soil under Hg pollution. This study compares the effects of Hg at different concentrations and exposure times on soil urease. Results indicate that soil urease activity was enhanced in the first two hours under low levels of Hg pollution, decreased after six hours of acute Hg pollution, and reached its maximum reduction in 24 hours. The urease in fluvo-aquic soil, with higher soil organic matter showed higher resistance to Hg acute pollution than that in red soil. Over a longer aging process, soil urease activity gradually recovered with time. Hormesis effects were observed in red soil under high Hg stress after 30 days, showing the strong resilience of urease enzyme function to Hg pollution. The ecological dose, ED10, (the Hg concentration causing a 10% reduction in soil urease activity) ranged from 0.09 to 0.59 mg kg-1 under short-term exposure, and was lower than that under a longer aging process (0.28 to 2.71 mg kg-1). Further, aging reduced the Hg ecotoxicity due to decreased Hg availability and the resilience of soil urease activity. This indicates that the risk of Hg pollution estimated by soil urease as an indicator depends on exposure time and enzyme stability. These factors need consideration in heavy metal pollution assessments using soil enzymes.

Enhancing water quality monitoring through the integration of deep learning neural networks and fuzzy method

The escalating growth of the global population has led to degraded water quality, particularly in seawater environments. Water quality monitoring is crucial to understanding the dynamic changes and implementing effective management strategies. In this study, water samples from the southwestern regions of Iran were spatially analyzed in a GIS environment using geostatistical methods. Subsequently, a water quality map was generated employing large and small fuzzy membership functions. Additionally, advanced prediction models using neural networks were employed to forecast future water pollution trends. Fuzzy method results indicated higher pollution levels in the northern regions of the study area compared to the southern parts. Furthermore, the water quality prediction models demonstrated that the LSTM model exhibited superior predictive performance (R2 = 0.93, RMSE = 0.007). The findings also underscore the impact of urbanization, power plant construction (2010 to 2020), and inadequate urban wastewater management on water pollution in the studied region.

Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: The HEI Experience and What's Next?

Although concentrations of ambient air pollution continue to decline in high-income regions, epidemiological studies document adverse health effects at levels below current standards in many countries. The Health Effects Institute (HEI) recently completed a comprehensive research initiative to investigate the health effects of long-term exposure to low levels of air pollution in the United States (U.S.), Canada, and Europe. We provide an overview and synthesis of the results of this initiative along with other key research, the strengths and limitations of the research, and remaining research needs. The three studies funded through the HEI initiative estimated the effects of long-term ambient exposure to fine particulate matter (PM2.5), nitrogen dioxide, ozone, and other pollutants on a broad range of health outcomes, including cause-specific mortality and cardiovascular and respiratory morbidity. To ensure high quality research and comparability across studies, HEI worked actively with the study teams and engaged independent expert panels for project oversight and review. All three studies documented positive associations between mortality and exposure to PM2.5 below the U.S. National Ambient Air Quality Standards and current and proposed European Union limit values. Furthermore, the studies observed nonthreshold linear (U.S.), or supra-linear (Canada and Europe) exposure-response functions for PM2.5 and mortality. Heterogeneity was found in both the magnitude and shape of this association within and across studies. Strengths of the studies included the large populations (7-69 million), state-of-the-art exposure assessment methods, and thorough statistical analyses that applied novel methods. Future work is needed to better understand potential sources of heterogeneity in the findings across studies and regions. Other areas of future work include the changing and evolving nature of PM components and sources, including wildfires, and the role of indoor environments. This research initiative provided important new evidence of the adverse effects of long-term exposures to low levels of air pollution at and below current standards, suggesting that further reductions could yield larger benefits than previously anticipated.

Assessing microbiological and heavy metal pollution in surface waters associated with potential human health risk assessment at fish ingestion exposure

Fish represent a significant source of nutrients but also cause negative health effects due to their bioaccumulation capacity for pollutants. The aim of this study was to examine the transfer of metals from the water of several rivers (Somes, Tisa, Sasar, Lapus, Lăpusel) to fish (Caras sp) tissue (subcutaneous fat, muscles, liver, intestines, kidneys, gills, brain, and eyes) and to identify and assess the carcinogenic and non-carcinogenic health risks of Arsenic (As), Cadmium (Cd), Nickel (Ni), Manganese (Mn), Cooper (Cu), Lead (Pb), Chromium (Cr) and Zinc (Zn) through the ingestion of fish (muscles and subcutaneous fat tissues). The obtained results indicated that a diet consisting of fish is particularly vulnerable, particularly in children compared to adults. The risk assessment results were below the threshold limit, although the fish samples contained heavy metals, with values exceeding the permitted limits of Fe (4.41-1604mg/kg), Cr (727-4155µg/kg), Zn (4.72-147mg/kg), and Ni (333-2194µg/kg). The studied surface waters are characterized by low and high degrees of pollution with heavy metals, as indicated by the heavy metal pollution index scores (HPI: 12.4-86.4) and the heavy metal evaluation index scores (HEI: 1.06-17.6). The considerable pollution levels are attributed to the high Mn content (0.61-49.7µg/kg), which exceeded the limit up to fifty times. A consistent set of physico-chemical analysis (pH, electrical conductivity, total hardness, turbidity, chloride, sulphate, nitrate, nitrite, ammonium, Ca, Mg, Na, K) was analysed in water samples as well. Considering the water quality index scores (WQI: 16.0-25.2), the surface waters exhibited good quality. Microbiological results indicated the presence of Listeria monocytogenes and atypical colonies of coagulase-positive staphylococcus in fish. In contrast, the surface waters from which fish samples were collected were positive for Escherichia coli, and coliform bacteria intestinal Enterococci. Based on the study’s results, it is recommended to exercise caution in the case of children related to the consumption of fish and using the waters for drinking purposes. This study provides important data of considerable novelty to the riparian population, researchers, and even policy makers on the quality status and potential levels of contamination of river waters, fish and the bioaccumulation of heavy metals in fish that may cause adverse effects on human health if consumed, and similarly the heavy metal pollution degree of waters and the non-carcinogenic risk of heavy metals through ingestion and skin absorption of water in children and adults (the study area is a significant source of fisheries).

ACPM Position Statement: Air Pollution & Environmental Justice

The American Lung Association's “State of the Air” 2023 report reveals almost 36% of Americans live with unhealthy levels of air pollution. Studies link air pollution with acute respiratory symptoms and exacerbation of respiratory and cardiovascular diseases. Differential air pollution exposures between white and non-white communities are significant components of environmental injustices. Even during the COVID-19 lockdown, when the United States experienced significant decreases in polluting activities, these differences persisted. The American College of Preventive Medicine's Science and Translation Committee conducted a nonsystematic literature review to explore initiatives addressing air pollution as a key component of environmental justice, the state of the science regarding health impacts, and evidence supporting mitigations to reduce those impacts. We recommend advocacy for cleaner energy sources and increasing green space; and increasing research, surveillance, and education and training on linkages between air pollutants and health. We recommend preventive medicine physicians raise awareness about increased risks of cardiovascular disease, cancer, asthma, and reduced lung function with air pollution exposure. Preventive medicine physicians may also educate patients and other practitioners about exposures, and how “conventional” disease prevention strategies may have unintended consequences; and influence healthcare leaders to improve efficiency and reduce emissions. We also recommend physicians utilize social determinants of health (SDOH) Z-Codes to capture environmental factors. Private payers should incorporate pollution exposure data into SDOH risk-adjustments for Medicare Advantage programs. Medicaid agencies should develop provider recommendations for pediatric populations, and states should finance in-home interventions for asthma.

Air pollution disproportionately impairs beneficial invertebrates: a meta-analysis

Air pollution has the potential to disrupt ecologically- and economically-beneficial services provided by invertebrates, including pollination and natural pest regulation. To effectively predict and mitigate this disruption requires an understanding of how the impacts of air pollution vary between invertebrate groups. Here we conduct a global meta-analysis of 120 publications comparing the performance of different invertebrate functional groups in unpolluted and polluted atmospheres. We focus on the pollutants ozone, nitrogen oxides, sulfur dioxide and particulate matter. We show that beneficial invertebrate performance is reduced by air pollution, whereas the performance of plant pest invertebrates is not significantly affected. Ozone pollution has the most detrimental impacts, and these occur at concentrations below national and international air quality standards. Changes in invertebrate performance are not dependent on air pollutant concentrations, indicating that even low levels of pollution are damaging. Predicted increases in tropospheric ozone could result in unintended consequences to global invertebrate populations and their valuable ecological services.

Enhanced electrochemical oxidation and machine learning-assisted sensing of tetrabromobisphenol A using activated carbon facilitated CoWO4 heterostructures

The escalating levels of environmental pollution, particularly from commercially used products, highlight the imperative of efficient sensor technologies to facilitate timely and effective remediation strategies. Herein, a simple method is proposed to enhance the electrochemical performance of CoWO4 structures by coupling them with activated carbon (AC) for direct tetrabromobisphenol A (TBPA) oxidation. The systematic comparison shows that incorporation of AC with CoWO4 not only improves the overall effective surface area (ESA) of the catalyst by 1.8-fold but also improves the cyclic voltammetry (CV) based irreversible oxidation current by 2.0-fold owing to improved conductivity and surface characteristics. Differential pulse voltammetry (DPV) based optimization of the sensory characteristics confirmed robust electrocatalytic TBPA oxidation within a 0.1 to 1.0 µM concentration range, achieving a 0.024 µM detection limit within PBS (0.1 M) (pH 5.0). Moreover, density functional theory (DFT) analysis confirms the non-covalent interaction favorability between TBPA and CoWO4 surface, validating TBPA’s easy adsorption onto the catalytic surface and, thus, facilitated electron mobility between the molecule and the catalyst during the surface oxidation process. The DPV sensing interpreted using machine learning (ML) algorithms confirmed the detection accuracy of the developed sensor. Among the adopted models, artificial neural networks (ANN) achieved the highest R2 score (0.9659) and the lowest values across Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) error matrices confirming its suitability in deciphering DPV correlations.Integrating ANN with DPV based electrocatalytic oxidation sensing underscores machine learning’s potential in analyzing complex electrochemical signatures, thus advancing intelligent sensing for precise environmental monitoring.

Scientific rationale for the indicators of biological urine monitoring in adolescents in Kazan City

BACKGROUND: Biomonitoring is crucial for assessing the level of pollution in a population, identifying risks and studying the impact of changes in technology. Knowledge of the reference values of trace elements in biological material is critical in comparing exposed with non-exposed groups and accurate. The control values of trace elements are crucial for ensuring public health and occupational safety. AIM: To study and analyze concentrations of priority chemical pollutants in the urine of adolescents aged 14–17 years in three zones in Kazan City and determine reference values for this age group. MATERIALS AND METHODS: The concentration of elements in urine of 276 adolescents aged 14–17 years was measured by inductively coupled plasma mass spectrometry. RESULTS: The upper 95 percentiles and their confidence intervals (RV95) were determined, which established the upper limit of the current background exposure to adolescents for 12 biomarkers, interpreted as “conditional” reference values for this age group. The following reference values for adolescents in Kazan City were obtained: Al, 12.80 µg/L; Cr, 1.02 µg/L; Mn, 3.53 µg/L; Hg, 0.65 µg/L; Cu, 110.91 µg/L, and Ni, 8.72 µg/L. In adolescents, the highest level of exceedance over background values was observed in zones 1 and 2 (Kirovsky and Privolzhsky Districts): Al exceeded by 1.4 times, Cr by 1.3 times in zone 1 (Kirovsky District), Mn by 1.5 times, Hg by 1.3 times, and Cu by 1.14 times in zone 2 (Privolzhsky district).The results are of interest for further analysis and identification of interrelations in the territory of the Republic of Tatarstan, Russia, where exposure levels may vary between regions or separate subgroups living in the same territory. CONCLUSION: Research in the field of biomonitoring in Kazan established the levels of exposure to toxic elements among the studied population and provided means for comparing exposure for further research by population groups by age, sex, and ethnicity. Moreover, biomonitoring results can be used to determine research priorities, measure exposure trends over time, and verify effectiveness of individual measures to combat pollution and other measures in the field of the environment and public health. The study presents modern technologies for assessing exposure to chemical pollutants using human biomonitoring methods and demonstrates its advantages.