Effects Of Pollution Research Articles

Effects of inorganic and organic pollution on development of Steindachneridion melanodermatum from the Iguaçu river, Brazil

The Iguaçu River basin, known for its rich endemic ichthyofauna, faces a significant threat from chemical pollution. The Jordão River, a major tributary, drains extensive agricultural and livestock areas and receives poorly treated domestic and industrial effluents before joining the Iguaçu River. This study investigated the pollution and toxic effects of water samples from the Iguaçu upstream (UI), Jordão (JR), and their confluence (DI) on Steindachneridion melanodermatum embryos and larvae. Chemical analyses of the water samples revealed that most contaminants were present at levels below detection limits or within the limits established by Brazilian legislation. However, cadmium in UI, aluminum in JR, and lead in DI exceeded the legal limits. Exposure up to 96 h post-fertilization revealed higher mortality and deformity rates in individuals exposed to water samples from UI and JR, despite JR having fewer detected pollutants. JR and DI samples induced increased superoxide dismutase activity, indicating activation of the antioxidant defense system due to xenobiotic exposure. Overall, the integrated biomarker response indexes showed that individuals exposed to JR water displayed the most significant variations in their responses compared to the control treatment, suggesting a higher level of contamination and toxicity. Although a direct link between water quality and toxicity in the Jordão and Iguaçu Rivers was inconclusive, the results confirmed pollution and risks to local wildlife. The study highlighted the harmful effects of pollutants, even at low concentrations. These findings underscore the need for conservation measures to safeguard endemic and endangered species in the Iguaçu River basin. Understanding pollutant effects on native species is crucial for effective mitigation strategies and ecological health preservation.

Optimizing stormwater runoff treatment: The role of two-stage tandem rain gardens

Regarded as a superior urban stormwater management solution, rain gardens can effectively store rainfall runoff and purify water quality. However, the efficiency of traditional rain gardens (TRG) in regulating runoff and removing nitrogen and phosphorus varies under different hydrological conditions. In this study, the TRG was retrofitted to construct a two-stage tandem rain garden (TTRG). Based on the experimental monitoring of rain gardens under natural rainfall from 2011 to 2013, results indicated a significantly higher runoff reduction capacity for the TTRG compared to the traditional garden (p<0.05), with average runoff and peak flow reduction rates increasing by 42.8% and 36.2%, respectively. Rainfall characteristics significantly impacted the runoff reduction of the TRG (p<0.05), but not the TTRG (p>0.05), demonstrating the enhanced control and stability of the TTRG in managing rainfall runoff. The concentration removal efficiency of nitrate nitrogen (NO3--N) was significantly improved (p<0.05), whereas the total phosphorus (TP), ammonium nitrogen (NH3-N) and total nitrogen (TN) were not significantly changed (p>0.05). The first-order kinetic model was used to fit the removal effect of different pollutants before and after retrofitting the rain garden, and the removal of NO3--N by the TTRG was better than that of the TRG. The TTRG showed significantly higher load removal efficiencies for TP, NO3--N, and NH3-N compared to TRG (p<0.05), with average load removal rates increasing by 49.92%, 75.02%, and 14.81%, respectively. The TTRG can regulate urban rainfall runoff more efficiently and stably. By changing the water flow path in the rain garden, the TTRG has a better runoff reduction ability and pollutant purification effect.

Relationships between water quality of a long-distance inter-basin water diversion project and air pollution emissions along the canal: Distributions, lag effects, and nonlinear responses

Understanding and quantifying the influences and contributions of air pollution emissions on water quality variations is critically important for surface water quality protection and management. To address this, we created a five-year daily data matrix of six water quality indicators—permanganate index (CODMn), NH3-N, pH, turbidity, conductivity, and dissolved organic matter (DOM)—and six air pollution indicators—O3, CO, NO2, SO2, 2.5 μm particulate matter (PM2.5), and inhalable particles (PM10)—using data from seven national monitoring stations along the world's longest water-diversion project, the Middle Route of the South-to-North Water Diversion Project in China (MR-SNWD). Multivariate techniques (Mann–Kendall, Spearman's correlation, lag correlation, and Generalized Additive Models [GAMs]) were applied to examine the nonlinear relationships and lag effects of air pollution on water quality. Air pollution and water quality exhibited marked spatial heterogeneity along the MR-SNWD, with all water quality parameters meeting Class I or II national standards and the air pollution indicators exceeding those thresholds. Except for CODMn and DOM, the other water quality and air pollution indicators exhibited significant seasonal differences. Air pollution exhibited significant lag effects on water quality at the northern stations, with NO2, SO2, PM2.5, and PM10 being highly correlated with changes in pH, with an average lag of 17 d. Based on the GAMs, lag effects enhanced the significant nonlinear relationships between air pollution and water quality, increasing the average deviance explained for CODMn, NH3-N, and pH by 93%, 24%, and 41%, respectively. These findings provide a scientific basis for protecting water quality along the long-distance inter-basin water-diversion project under anthropogenic air pollution.

Heavy metals distribution and their ecological risk in the surface sediments of Haizhou Bay, China

A comprehensive analysis of heavy metal concentrations, sources, and pollution status was conducted on thirty surface sediment samples collected from Haizhou Bay, China in 2020. The spatial distributions of Cu, Pb, Zn, Cr, Cd, and total organic carbon (TOC) were found to be analogous. Noteworthy, sporadic pollution effects from Cu, Cr, and As were detected, although heavy metal concentrations remained below the probable effect level (PEL). A suite of analytical methods, including Pearson's correlation coefficient, geoaccumulation index, sediment quality guidelines, potential ecological risk index, and principal component analysis, was employed to elucidate the sources and assess the pollution risks. The results indicated that Hg emerged as the predominant heavy metal pollutant in Haizhou Bay sediments, with several stations showing high pollution levels that require attention. Other heavy metals were mainly of natural origin, indicating no significant pollution in the study area.

Assessment of the Respiratory Disease Mortality Risk from Single and Composite Exposures to PM2.5 and Ozone - Guangzhou City, Guangdong Province, China, 2018-2021.

Fine particulate matter (PM2.5) and ozone (O3) are prevalent pollutants in the atmosphere, which threaten human health, especially the respiratory system. Typically, people are exposed to a mixture of various pollutants in the environment. Thus, the single and combined effects of both pollutants need to be investigated. PM2.5 and O3 increase the risk of death from lung cancer, chronic obstructive pulmonary disease (COPD), and respiratory diseases, with their lagged and cumulative effects analyzed, indicating an acute effect. In addition, combined exposure to both pollutants can significantly affect disease deaths. This study provides further evidence of the single and combined effects of PM2.5 and O3 on respiratory diseases, emphasizing the need for sustained efforts in air pollution control, with greater attention to the synergistic management of air pollutants.

A comparison study on membrane fouling in A/O-MBR and A/A-MBR at different mixed liquor-suspended solids concentrations

ABSTRACT Membrane fouling leads to decreased membrane flux, increases the frequency of membrane tissue replacement and membrane cleaning, and increases the operating cost of membrane bioreactor. In this study, the pollutant removal effects, membrane fouling differences and microbial characteristics of anaerobic/aerobic MBR (A/O-MBR) and anaerobic/anoxic MBR (A/A-MBR) were investigated at different mixed liquor suspended solids (MLSS) concentrations. The results showed that the chemical cleaning cycle of membrane contamination was 12, 28, 44 h and 24, 40, 104 h, respectively, and the cycle was prolonged with the increase of MLSS concentration (from 6000 to 9000 mg L−1). A/O-MBR was 1.4–2.4 times the rate of membrane fouling of A/A-MBR. In irreversible resistance, extracellular polymer substances (EPS) were the most significant contributors to membrane fouling. EPS concentration in A/A-MBR (118.33, 73.75, 54.26 mg/gMLSS) was lower than that in A/O-MBR (171.68, 91.92, 62.33 mg/gMLSS). Therefore, increasing MLSS concentration could mitigate membrane fouling. 16S rRNA high-throughput sequencing demonstrated that filamentous bacteria was the primary reason for the membrane fouling difference. Filamentous bacteria were more likely to be attached to the surface of the membrane, causing membrane fouling. The abundance percentage of filamentous bacteria in A/A-MBR was smaller than that in A/O-MBR. In summary, The excellent performance of A/A-MBR in membrane fouling behaviour, resistance analysis, EPS and microorganisms proved that A/A-MBR is more promising than A/O-MBR in wastewater nitrogen and phosphorus removal. This study can provide a theoretical basis for the application of MBR in the field of sewage treatment.

Assessing LDPE microplastics' impact on green gram (Vigna radiata L. Wilczek) cultivation: A greenhouse pot experiment

Plastic pollution, particularly from microplastics (MPs), poses a significant threat to agricultural ecosystems. This study investigates the impact of Low-Density Polyethylene (LDPE) microplastics on green gram (Vigna radiata) cultivation, focusing on growth, yield, and soil nutrient dynamics. A completely randomized design with nine treatments of varying LDPE concentrations was used in a controlled poly house environment. Soil analysis assessed mechanical and physico-chemical properties using established methods. Green gram variety Co (Gg) 8 was cultivated during the Rabi season, with comprehensive crop management including germination tests, pot culture preparations, precise fertilizer application, and essential cultural operations. Results indicated significant correlations between LDPE concentrations and green gram growth parameters, with higher concentrations having detrimental effects (correlation coefficient = −0.75, p < 0.05). LDPE treatments also influenced plant nutrient uptake, showing inverse correlations with nitrogen (N), phosphorus (P), and potassium (K) uptake (correlation coefficient = −0.82, p < 0.01). Post-harvest soil analysis revealed significant variations in available nutrients, with LDPE-treated soils exhibiting higher N, P, and K levels compared to control groups (correlation coefficient = 0.78, p < 0.01). Infrared spectroscopy confirmed the presence of LDPE microplastics in soil and plant samples, identified by characteristic (CO) and (C–H) stretching vibrations. These findings highlight the complex interactions between LDPE microplastics and agricultural systems, emphasizing the need for sustainable practices to mitigate the adverse effects of plastic pollution on agriculture. Statistical analysis using the CRD framework and correlation and regression analyses ensured robust interpretation of the data.

Sex disparities in the burden of air particulate matter-related unhealthy years and life-years lost in Asia-Pacific countries, 1990–2019

Air quality, particularly in the Asia-Pacific region (APAC), significantly impacts human health and mortality. This study aims to quantify and compare the effects of ambient particulate matter (APM) and household air pollution (HAP) from solid fuels on unhealthy years and life-years lost in APAC from 1990 to 2019. It also examines factors influencing unhealthy years among females due to HAP. Our findings show that females were more vulnerable to HAP, whereas males were at a higher mortality risk from APM. Pacific islands encountered the greatest burden from HAP, while West Asia was most affected by APM. Over the studied period, the impact of APM on unhealthy years and life-years lost increased, primarily affecting the elderly and adults more than children and youth. Conversely, health impacts from HAP declined across all age groups in the Pacific Islands, though less so compared to other subregions. Key predictors of female unhealthy years from HAP included pollution, demographics, and women's empowerment, with no significant economic influences. Understanding these impacts, along with age and gender differences, is crucial for developing targeted environmental health policies and interventions.

Impact of Air Pollutants on Lung Function and Inflammatory Response in Asthma in Shanghai.

Air pollution is a leading public health issue. This study investigated the effect of air quality and pollutants on pulmonary function and inflammation in patients with asthma in Shanghai. The study monitored 27 asthma outpatients for a year, collecting data on weather, patient self-management [daily asthma diary, peak expiratory flow (PEF) monitoring, medication usage], spirometry and serum markers. To explore the potential mechanisms of any effects, asthmatic mice induced by ovalbumin (OVA) were exposed to PM 2.5. Statistical and correlational analyses revealed that air pollutants have both acute and chronic effects on asthma. Acute exposure showed a correlation between PEF and levels of ozone (O 3) and nitrogen dioxide (NO 2). Chronic exposure indicated that interleukin-5 (IL-5) and interleukin-13 (IL-13) levels correlated with PM 2.5 and PM 10 concentrations. In asthmatic mouse models, exposure to PM 2.5 increased cytokine levels and worsened lung function. Additionally, PM 2.5 exposure inhibited cell proliferation by blocking the NF-κB and ERK phosphorylation pathways. Ambient air pollutants exacerbate asthma by worsening lung function and enhancing Th2-mediated inflammation. Specifically, PM 2.5 significantly contributes to these adverse effects. Further research is needed to elucidate the mechanisms by which PM 2.5 impacts asthma.

Prospective study on the joint effect of persistent organic pollutants and glucose metabolism on chronic kidney disease: Modifying effects of lifestyle interventions

There is no evidence on the associations between persistent organic pollutants (POPs) and the incidence of chronic kidney disease (CKD) in the Chinese rural population. We aimed to investigate the individual and mixed effects of 22 POPs on the prevalence and incidence of CKD, and the joint effects of POPs and abnormal glucose metabolism as well as the modification effects of healthy lifestyle on these associations. A total of 2775 subjects, including 925 subjects with normal plasma glucose (NPG) and 925 subjects with prediabetes (PDM) and type 2 diabetes mellitus (T2DM), were enrolled from the Henan Rural Cohort Study. Logistic regression and quantile g-computation were performed to assess the individual and mixed effects of POPs on the risk of CKD. Joint effects of POPs and abnormal glucose metabolism status, as well as the modification effects of lifestyle on CKD were assessed. After 3-year follow-up, an increment of ln-o,p’-DDT was related to an elevated risk of CKD prevalence. Positive associations of p,p’-DDE and β-BHC with CKD incidence were observed (P < 0.05). In addition, participants with high levels of ∑POPs were associated elevated incidence risk of CKD (OR: 1.217, 95%CI: 1.008–1.469). One quartile increase in POPs mixture was associated with the increased incidence of CKD among T2DM patients (P < 0.05). Further, a higher risk of CKD was observed among PDM and T2DM patients with high levels of o,p’-DDT, p,p’-DDE, β-BHC, and ∑POPs than NPG subjects with low levels of pollutants. In addition, interactive effects of ∑POPs and lifestyle score on CKD incidence were found. Individual and mixed exposure to POPs increased the prevalence and incidence of CKD, and glucose metabolic status exacerbated the risk of CKD resulting from such exposures. Further, the modifying effects of lifestyle were observed, highlighting the importance of precision prevention for high-risk CKD population and healthy lifestyle intervention measures.

A confounding bridge approach for double negative control inference on causal effects

Unmeasured confounding is a key challenge for causal inference. In this paper, we establish a framework for unmeasured confounding adjustment with negative control variables. A negative control outcome is associated with the confounder but not causally affected by the exposure in view, and a negative control exposure is correlated with the primary exposure or the confounder but does not causally affect the outcome of interest. We introduce an outcome confounding bridge function that depicts the relationship between the confounding effects on the primary outcome and the negative control outcome, and we incorporate a negative control exposure to identify the bridge function and the average causal effect. We also consider the extension to the positive control setting by allowing for the nonzero causal effect of the primary exposure on the control outcome. We illustrate our approach with simulations and apply it to a study about the short-term effect of air pollution on mortality. Although a standard analysis shows a significant acute effect of PM2.5 on mortality, our analysis indicates that this effect may be confounded, and after double negative control adjustment, the effect is attenuated toward zero.

ABUNDANCE AND PHYLOGENETIC ANALYSIS OF HIGH-DENSITY POLYETHYLENE (HDPE) BIODEGRADING BACTERIA FROM BRANTAS RIVER, MALANG CITY

Microplastic pollution has become a global concern after Pandemic COVID-19. This requires efforts to resolve the various negative effects of microplastic pollution in the aquatic environment. The purpose of this study was to isolate, enumerate and potency of microplastic biodegrading bacteria from the Brantas River, Malang City. These research steps consist of environmental factor measure, bacterial isolation, enumeration, biodegradation test (qualitative and quantitative) and data analysis. The environmental conditions of the sampling locations are generally homogeneous. The river temperature observed ranged from 24.1 to 27.2 °C. Research locations that have acidity ranging from 7.35 to 8.16. The salinity of the Brantas River ranges from 205-306 ppm or 0.0205-0.306%. The conductivity of the Brantas River ranges from 410-612 µS/cm. Isolation produced 6 different bacterial isolates. The amount of each isolate varies in each sample. Each isolate was characterized according to colony character standards (configurations, margins and elevations) and cells (shape, gram). Microplastic biodegradation qualitative test indicated by the growth of colonies in the plastic area. This indicates that the bacterial isolate uses plastic as a carbon source. All isolates grow around plastic. Quantitative tests using HDPE sheets show that not all isolates are capable of biodegrading microplastics. Bacterial isolates capable of biodegrading HDPE plastic were isolates K2 (5.41%), K22 (5.16%) and K5 (6.69%).

Accurate PM2.5 urban air pollution forecasting using multivariate ensemble learning Accounting for evolving target distributions

Over the past decades, air pollution has caused severe environmental and public health problems. According to the World Health Organization (WHO), fine particulate matter (PM2.5), a key component reflecting air quality, is the fourth leading cause of death worldwide after cardiovascular disease, smoking, and diet. Various research efforts have aimed to develop PM2.5 forecasting models that can be integrated into a solution to mitigate the adverse effects of air pollution. However, PM2.5 forecasting is challenging because air pollution data are non-stationary and influenced by multiple random effects. This paper proposes an effective multivariate multi-step ensemble machine learning model for predicting continuous 24-h PM2.5 concentrations, considering meteorological conditions, the rolling mean of PM2.5 time series, and temporal features. PM2.5 is strongly correlated with space and time. Therefore, forecasting results from one location are insufficient to represent the level of air pollution for an entire city. In this study, we established six real-time air quality monitoring sites in different regions, including traffic, residential, and industrial areas in Ho Chi Minh City (HCMC), and generated forecasting results for each station. Various statistical methods are incorporated to evaluate the performance of the model. The experimental results confirm that the model performs well, substantially improving its forecasting accuracy compared to existing PM2.5 forecasting models developed for HCMC. In addition, we analyze to determine the contribution of different feature groups to model performance. The model can serve as a reference for citizens scheduling local travel and for healthcare providers to provide early warnings.

Forecasting ground-level ozone and fine particulate matter concentrations at Craiova city using a meta-hybrid deep learning model

Air quality forecasting is vital for managing and mitigating the adverse effects of air pollution on human health, crops, and the environment. This study aims to forecast daily time series of ozone and fine particulate matter (PM) concentrations using a meta-hybrid deep NARMAX (Nonlinear Auto-Regressive Moving Average with eXogenous inputs) model. Two datasets were utilised: (a) data on meteorological parameters (temperature, air pressure, relative humidity) and air pollutant concentrations (particulate matter, ozone, dioxide of carbon, volatile organic compounds, formaldehyde) provided by a sensor model A3 situated in the centre of Craiova city, and (b) data on wind direction, wind speed, and sunshine duration provided by the National Meteorological Administration. The data sets covered a time interval from December 10, 2020, to January 05, 2024. Initially, a statistical analysis was conducted to assess the correlation between variables. Results revealed that ozone concentration is primarily influenced by meteorological variables such as temperature (r = 0.79), sunshine duration (r = 0.55), and relative humidity (r = −0.48), and secondarily by air pollution indicators including VOC (r = −0.34), PM concentrations (r = −0.34), and CO2 (r = −0.3). In the subsequent stage, thirteen Machine Learning (ML) models were employed in conjunction with an integral feature selection (IFS) method to identify the best combinations of predictor variables for predicting ozone and PMs. Finally, a deep NARMAX model was developed to forecast the next periods of ozone and PMs based on the optimal combinations identified earlier. Results indicated the selection of sixty best models for ozone forecasting and four best models for PMs. The R2 values surpassed 0.97 for ozone and exceeded 0.8 for PMs, demonstrating the efficacy of the forecasting approach.

Exposure to green and blue spaces and depression among older adults from the EPIPorto cohort: examining environmental, social, and behavioral mediators and varied space types

ABSTRACT This study investigated the associations between exposure to different types of natural spaces and depression among urban older adults and explored the potential mediators. We used data on 631 participants aged ≥60 from the EPIPorto cohort (Porto, Portugal). Depressive symptoms were measured using the Geriatric Depression Scale. We assessed greenness in residential surroundings using the Normalized Difference Vegetation Index (NDVI) and evaluated accessibility to green and blue spaces. Metrics were calculated separately for agricultural, natural, and urban green spaces. We used logistic regression to estimate associations and mediation analysis to examine the mediating effects of air pollution, temperature, physical exercise, social support, and social activities. Living near urban green spaces was associated with lower odds of depression (OR = 0.21, 95% CI 0.03–0.74). Contrastingly, natural and agricultural green spaces were associated with higher odds of depression (OR = 1.69, 1.02–2.79 and OR = 1.80, 1.23–2.64, respectively). No associations were found for NDVI, accessibility to blue spaces, or green space size. Social support explained a significant proportion (33.9%, p = 0.044) of the total effect between the type of nearest green space and depression. These findings suggest that enhancing the accessibility of urban green spaces holds promise as a strategy for mitigating depression among older adults.

Long-term effects of air pollution on health in the five largest cities in Colombia

Long-term effects of air pollution on health in the five largest cities in Colombia

Effects of Air Pollution on Pediatric Incident Seizure: A Case-crossover Analysis

Effects of Air Pollution on Pediatric Incident Seizure: A Case-crossover Analysis

Particulate matter and bioactive lipid mediators: Insights into the mechanisms of the cardiovascular effects of particulate air pollution

Particulate matter and bioactive lipid mediators: Insights into the mechanisms of the cardiovascular effects of particulate air pollution

Scoping Review on Health Effects of Air Pollution in East Africa

Scoping Review on Health Effects of Air Pollution in East Africa

A novel concern from two sample Mendelian randomization study: The effects of air pollution exposure on the cardiovascular, respiratory, and nervous system

BackgroundCardiovascular, respiratory, and nervous system diseases have high morbidity and mortality rates, but the causal relationship between air pollution and these diseases remains controversial. MethodsWe conducted a large-scale genome-wide association (GWAS) study using Mendelian randomization (MR) to investigate the association between air pollution like Nitrogen dioxide (NO2), Nitrogen oxides (NOX), Particulate matter with diameter<2.5μm (PM2.5), Particulate matter with diameter<10μm (PM10) and cardiovascular, respiratory, and nervous system diseases, including acute myocardial infarction, heart failure, asthma, chronic obstructive pulmonary disease (COPD), pneumonia, stroke and Parkinson's disease. This study included 337,199 patients with acute myocardial infarction, 178,726 patients with heart failure, 463,010 patients with asthma, 462,933 patients with COPD, 486,484 patients with pneumonia, 484,598 patients with stroke, and 482,730 patients with Parkinson's disease. All genetic tools were identified from GWAS. The association effects of environmental pollution and these diseases were investigated using MR analysis, sensitivity analysis with heterogeneity, pleiotropy test, and leave-one-out test. ResultsOur MR analysis showed the association between NOX and the development of COPD and stroke (Odds ratio (OR)=1.010, 95 % Confidence interval (CI): 1.000～1.020, P=0.046; OR=1.017, 95 %CI:1.003–1.031, P=0.019), the association between PM2.5 and the development of asthma, COPD and stroke (OR=1.013, 95 %CI:1.003–1.024, P=0.011; OR=1.010, 95 %CI:1.000–1.019, P=0.035; OR=1.019, 95 %CI:1.004–1.033, P=0.012). No significant associations were found between the rest of the air pollution exposures and diseases. Leave-one-out sensitivity analysis showed stable results. ConclusionsThe study clarifies the relationship between air pollution and cardiovascular, respiratory, and nervous system diseases, providing valuable evidence for environmental pollution prevention and population health monitoring, and provides a clear direction and evidence for the subsequent investigation of the association between air pollution and diseases.