Identification Of Pollution Sources Research Articles

Pollution Characteristics, Risk Assessment and Source Identification of Heavy Metals in Surface Sediments from the Cage Fish Farm of Poyang Lake, China

ABSTRACT With the development of cage culture, increasing heavy metals concentrations are being taken into sediments. The accumulation of heavy metals in sediments may cause long term environmental issues and pose threat to human health. Fourteen surface sediment samples were collected to assess the contamination status and potential ecological risk of heavy metals, and identify their potential sources from the cage fish farm of Poyang Lake. The concentrations of Cu, Pb, Zn, Cd, Cr, As and Ni in the sediments were measured by ICP-MS. The geo-accumulation index, enrichment factor and potential ecological risk index were implied to evaluate their pollution levels and ecological risk. The principal component analysis was used to identify their potential sources. The results showed that all of the examined heavy metals content exceeded their background values. These heavy metals (except Cd, Cr and As) presented moderately polluted level. Cu showed significant enrichment, Zn, Pb and Ni exhibited moderate enrichment, As and Cd presented minimal enrichment. Cu and Cd posed a moderate ecological risk, and the other heavy metals posed a low ecological risk. The comprehensive potential ecological risk was associated with considerable ecological risk. Cage culture, smelting, mining and agriculture activities were their primary pollution sources.

Pinpointing sources of pollution using citizen science and hyperlocal low-cost mobile source apportionment

Currently, methodologies for the identification and apportionment of air pollution sources are not widely applied due to their high cost. We present a new approach, combining mobile measurements from multiple sensors collected from the daily walks of citizen scientists, in a high population density area of Birmingham, UK. The methodology successfully pinpoints the different sources affecting the local air quality in the area using only a handful of measurements. It was found that regional sources of pollution were mostly responsible for the PM2.5 and PM1 concentrations. In contrast, PM10 was mostly associated with local sources. The total particle number and the lung deposited surface area of PM were almost solely associated with traffic, while black carbon was associated with both the sources from the urban background and local traffic. Our analysis showed that while the effect of the hyperlocal sources, such as emissions from construction works or traffic, do not exceed the distance of a couple of hundred meters, they can influence the health of thousands of people in densely populated areas. Thus, using this novel approach we illustrate the limitations of the present measurement network paradigm and offer an alternative and versatile approach to understanding the hyperlocal factors that affect urban air quality. Mobile monitoring by citizen scientists is shown to have huge potential to enhance spatiotemporal resolution of air quality data without the need of extensive and expensive campaigns.

Nitrogen Transport Pathways and Source Contributions in a Typical Agricultural Watershed Using Stable Isotopes and Hydrochemistry

The increasing global nitrogen input poses a significant threat to aquatic environments, particularly in agricultural watersheds, where intensive human activities and insufficient water protection infrastructure exacerbate the risk of nitrogen pollution. Accurate identification of nitrogen pollution sources and the associated transformation processes is essential for protecting watershed ecosystems. In this study, a combination of hydrochemical analysis, correlation and principal component analysis, and stable nitrate isotopes (δ15N-NO3− and δ18O-NO3−) were employed to trace nitrogen transport pathways and source contributions in both surface water and groundwater within a typical agricultural watershed. The results revealed the presence of nitrogen pollution, including total nitrogen (TN), ammonia nitrogen (NH3-N), and nitrate nitrogen (NO3−-N), with significant spatial and seasonal variations in both surface water and groundwater. The spatiotemporal evolution of hydrochemical indicators and nitrate isotope compositions highlighted multiple potential sources of nitrogen, including soil input, agricultural input, and manure and sewage input. The results from stable isotope analysis in an R (SIAR) model indicated that ammonium fertilizers (7.1%~78.4%) and manure and sewage (2.6%~69.7%) were the primary sources of nitrates in surface water, while manure and sewage were the main sources in groundwater (67.9%~73.7%). This research demonstrated that nitrification, seasonal variations, and human activities significantly impact nitrogen migration and transformation in agricultural watersheds. However, the issue of groundwater severely polluted by manure and sewage has received insufficient attention. To effectively control nitrogen pollution in agricultural watersheds, it is necessary to improve septic tanks and sewage networks, as well as implement scientific fertilization practices.

A framework integrating affinity propagation algorithm and spatial bivariate analysis for enhanced identification and localisation of soil heavy metals pollution sources.

The accurate identification of pollutant sources and their spatial distribution is crucial for mitigating soil heavy metals (SHMs) pollution. However, the receptor model struggles to effectively categorize pollutant sources and pinpoint their locations and dispersion trends. We propose a novel comprehensive framework that combines a receptor model, random forest (RF), affinity propagation (AP) algorithm, and bivariate local indicator of spatial association (BLISA), to optimize the traditional approach for tracing SHMs sources in industrial regions. We apportioned SHMs sources using a receptor model combined with RF, while BLISA combined with AP methods were employed to accurately locate the source areas and identify their dispersion tendencies. The results revealed that SHMs originated from mixed sources of equipment manufacturing agglomeration and agricultural activities (59.0%), geological background (30.5%), and emissions from heavily-polluting industries (10.5%). The pollution sources of soil Cd and Pb were located near specific industries, showing characteristics of multi-site concurrent pollution diffusion influenced by their proximity to industrial sites. The spatial distribution of Cr, Cu, and Zn sources was concentrated in high-density urban industrial areas, transitioning from point to nonpoint sources, with diffusion patterns influenced by the spatial agglomeration effect of industries. Our enhanced framework accurately identifies the location of SHMs sources and their dispersion tendencies, thereby improving regional soil pollution management.

Unveiling urban air quality dynamics during COVID-19: a Sentinel-5P TROPOMI hotspot analysis

In India, the spatial coverage of air pollution data is not homogeneous due to the regionally restricted number of monitoring stations. In a such situation, utilising satellite data might greatly influence choices aimed at enhancing the environment. It is essential to estimate significant air contaminants, comprehend their health impacts, and anticipate air quality to safeguard public health from dangerous pollutants. The current study intends to investigate the spatial and temporal heterogeneity of important air pollutants, such as sulphur dioxide, nitrogen dioxide, carbon monoxide, and ozone, utilising Sentinel-5P TROPOMI satellite images. A comprehensive spatiotemporal analysis of air quality was conducted for the entire country with a special focus on five metro cities from 2019 to 2022, encompassing the pre-COVID-19, during-COVID-19, and current scenarios. Seasonal research revealed that air pollutant concentrations are highest in the winter, followed by the summer and monsoon, with the exception of ozone. Ozone had the greatest concentrations throughout the summer season. The analysis has revealed that NO2 hotspots are predominantly located in megacities, while SO2 hotspots are associated with industrial clusters. Delhi exhibits high levels of NO2 pollution, while Kolkata is highly affected by SO2 pollution compared to other major cities. Notably, there was an 11% increase in SO2 concentrations in Kolkata and a 20% increase in NO2 concentrations in Delhi from 2019 to 2022. The COVID-19 lockdown saw significant drops in NO2 concentrations in 2020; specifically, − 20% in Mumbai, − 18% in Delhi, − 14% in Kolkata, − 12% in Chennai, and − 15% in Hyderabad. This study provides valuable insights into the seasonal, monthly, and yearly behaviour of pollutants and offers a novel approach for hotspot analysis, aiding in the identification of major air pollution sources. The results offer valuable insights for developing effective strategies to tackle air pollution, safeguard public health, and improve the overall environmental quality in India. The study underscores the importance of satellite data analysis and presents a comprehensive assessment of the impact of the shutdown on air quality, laying the groundwork for evidence-based decision-making and long-term pollution mitigation efforts.

Mechanisms of evolution and pollution source identification in groundwater quality of the Fen River Basin driven by precipitation

Groundwater pollution has attracted widespread attention as a threat to human health and aquatic ecosystems. However, the mechanisms of pollutant enrichment and migration are unclear, and the spatiotemporal distributions of human health risks are poorly understood, indicating insufficient groundwater management and monitoring. This study assessed groundwater quality, human health risks, and pollutant sources in the Fen River Basin(FRB). Groundwater quality in the FRB is good, with approximately 87 % of groundwater samples rated as “excellent” or “good” in both the dry and rainy seasons. Significant precipitation elevates groundwater levels, making it more susceptible to human activities during the rainy season, slightly deteriorating water quality. Some sampling points in the southern of Taiyuan Basin are severely contaminated by mine drainage, with water quality index values up to 533.80, over twice the limit. Human health risks are mainly from As, F, NO3−, and Cr. Drinking water is the primary pathway of risk. From 2019 to 2020, the average non-carcinogenic risk of As, F, and NO3− increased by approximately 28 %, 170 % and 8.5 %, respectively. The average carcinogenic risk of As and Cr increased by 28 % and 786 %, the overall trend of human health risks is increasing. Source tracing indicates As and F mainly originate from geological factors, while NO3− and Cr are significantly influenced by human activities. Various natural factors, such as hydrogeochemical conditions and aquifer environments, and processes like evaporation, cation exchange, and nitrification/denitrification, affect pollutant concentrations. A multi-tracer approach, integrating hydrochemical and isotopic tracers, was employed to identify the groundwater pollution in the FRB, and the response of groundwater environment to pollutant enrichment. This study provides a scientific basis for the effective control of groundwater pollution at the watershed scale, which is very important in the Loess Plateau.

Quantifying seasonal variations in pollution sources with machine learning-enhanced positive matrix factorization

As the pace of industrialization and urbanization accelerates, water quality management faces increasing challenges, with traditional methods for pollutant source apportionment often proving inadequate in handling complex environmental data. This study enhances the precision and reliability of pollutant source identification by integrating Positive Matrix Factorization (PMF) models with diverse machine learning techniques. Utilizing data from 17 water quality monitoring stations along the Wuding River from 2017 to 2021, we employed Random Forest (RF), Support Vector Machine (SVM), Elastic Net (EN), and Extreme Gradient Boosting (XGBoost) models to predict the Water Quality Index (WQI) during dry and wet seasons. Results indicate that the RF model exhibited optimal performance in the dry season (R2 = 0.93), while the SVM was superior in the wet season (R2 = 0.94). SHAP (SHapley Additive exPlanations) value analysis identified CODMn and NH3-N as significant influencers on WQI in the dry season, whereas COD, BOD, and TP gained prominence during the wet season. SHAP values reveal the contribution of each feature to the model output, thereby enhancing the model’s transparency and interpretability. Additionally, feature importance identified by machine learning was utilized as weights to optimize the contribution rates predicted by the PMF model. The optimised model was able to identify the contribution of domestic and farm effluent discharges more accurately in the dry season, with a significant increase in the percentage of identification from 19.4 % to 45.4 %, and an increase in the percentage of contribution from agricultural non-point sources and domestic effluent in the rainy season. This research offers a novel perspective on the characteristics of river water pollution and holds significant implications for formulating data-driven environmental management strategies.

Crucial time of emergency monitoring for reliable numerical pollution source identification

The Pollution source identification (PSI) is an important issue on river water quality management especially for urban receiving water. Numerical inversion method is theoretically an effective PSI technique, which employs monitored downstream pollutant breakthrough curves to identify the pollution source. In practice, it is important to know how much monitoring data should be accumulated to provide PSI results with acceptable accuracy and uncertainty. However, no literature reports on this key point and it seriously handers the numerical PSI technology to mature practical applications. To seek a monitoring guideline for PSI, we conducted extensively numerical experiments for single-point source instantaneous release taking Bayesian-MCMC method as the baseline inversion technique. The crucial time (Tc) phenomenon was found during the data accumulation process for Bayesian source inversion. After Tc, estimated source parameters subsequent sustained low error levels and uncertainty convergence. Results shown the presence of Tc impacted by the number and location of monitoring sections, while monitoring frequency and data error do not. Under different river hydrodynamic conditions, relative crucial time (Λ) is determined by the river's Peclet number, and minimum effective Λ was controlled by dispersion coefficient (Dx). Analytic spatial structure of Λ(U, Dx) was uncovered and this relationship successfully explained by the information entropy theory. Based on these findings, a novel design method of PSI emergency monitoring network for preparedness plan and a practical framework of PSI for emergency response were established. These findings fill the important knowledge gap in PSI applications and the guidelines provide valuable references for river water quality management.

Seasonal variations and the prevalence of phenolic profiles in ambient fine particulate matter and their impact on oxidative potential

Exposure to fine particulate matter (PM2.5) poses numerous health risks, with oxidative potential (OP) serving as a critical marker of its toxicity. Synthetic phenolic antioxidants (SPAs) and bisphenols (BPs) influence reactive oxygen species (ROS) levels in PM2.5, and exposure to these compounds induces oxidative stress in organisms, thereby potentially affecting the OP of PM2.5. We detected 26 phenols (including 12 SPAs, 5 transformation products (TPs), and 9 BPs) in PM2.5 sample collected from October 2018 to September 2021 in Wuhan, China. Among them, 19 substances were detected at a detection frequency greater than 50 % in PM2.5 sample. AO 2246 and BHT were the main components of SPAs, and BHT-Q and BPA had the highest concentrations in TPs and BPs, respectively. PM2.5 mass concentrations and phenolic levels were higher in winter and autumn. Substances within groups were strongly correlated, suggesting the same or similar source of exposure. This finding aid in more precise pollution source identification and is crucial for comprehensively evaluating their combined health effects. Furthermore, we determined the OP of PM2.5 and found that BPs were related to increased OP and ROS. This suggests that the toxicity of PM2.5 is influenced not only by its concentration but also by its chemical composition, with BPs potentially enhancing its toxic effects. These factors should be fully considered when assessing the health impacts of PM2.5.

International airport emissions and their impact on local air quality: chemical speciation of ambient aerosols at Madrid–Barajas Airport during the AVIATOR campaign

Abstract. Madrid–Barajas Airport (MAD) is the fourth-busiest airport in Europe. The aerosol chemical composition and the concentrations of other key pollutants were measured at the airport perimeter during October 2021 to assess the impact of airport emissions on local air quality. A high-fidelity ambient instrumentation system was deployed at Madrid–Barajas Airport to measure the following: concentrations of organic aerosols (with their composition), black carbon (eBC), carbon dioxide (CO2), carbon monoxide (CO), nitrogen dioxide (NOx), sulfur dioxide (SO2), particulate matter (PM2.5, PM10), total hydrocarbon (THC), and total particle number. The average concentrations of eBC, NOx, SO2, PM2.5, PM10, CO, and THC at the airport for the entire campaign were 1.07 µg m−3, 22.7 µg m−3, 4.10 µg m−3, 9.35 µg m−3, 16.43 µg m−3, 0.23 mg m−3, and 2.30 mg m−3, respectively. The source apportionment analysis of the non-refractory organic aerosol (OA) using positive matrix factorisation (PMF) allowed us to discriminate between different sources of pollution, namely less oxidised oxygenated organic aerosol (LO-OOA), alkane organic aerosol (AlkOA), and more oxidised oxygenated organic aerosol (MO-OOA). The results showed that LO-OOA and MO-OOA account for more than 80 % of the total organic particle mass measured near the runway. Trace gases correlate better with the AlkOA factor than LO-OOA and MO-OOA, indicating that AlkOA is mainly related to primary combustion emissions. Bivariate polar plots were used for pollutant source identification. Significantly higher concentrations of the obtained factors were observed at low wind speeds (<3 m s−1) from the southwest, where two of the runways and all terminals are located. Higher SO2/NOx and CO/eBC ratios were observed when the winds originated from the northeast, where the two northern runways are located. These elevated ratios are attributed to the aircraft activity being the major pollutant source in the northeast area.

A multi-analytical approach for the identification of pollutant sources on black crust samples: Stable isotope ratio of carbon, sulphur, and oxygen

This study is focused on the identification of pollutant sources on black crust (BC) samples from the Monumental Cemetery of Milan (Italy), through a multi-analytical approach based on the determination of stable isotope ratios of carbon, sulphur, and oxygen. Six black crust samples, mainly developed on marble sculptures over a time span of 100–150 years, were analysed.For the first time, δ13C was measured for BC samples: δ13C values of the pulverized samples (from −1.2 to +1.3 ‰) are very close to the values obtained from the carbonate matrix, whereas after the removal of the matrix through acidification, δ13C values of BC samples from Milan range from −27.2 to −22.1 ‰, with no significant variation between samples with different ratios of organic carbon to elemental carbon. In sum, the δ13C values obtained for all BC samples fall within the range of anthropogenic emissions such as vehicle traffic, coal combustion and industrial emissions.δ34S and δ18O values of sulphate from BC samples range from −6.3 to +7.0 ‰ and from +7.6 to +10.5 ‰, respectively. Coupling the analysis of the oxygen isotope ratio with that of sulphur enables a more precise identification of the origin of sulphates: the observed isotopic composition falls in the range typical for anthropogenic emission of sulphur dioxide.Overall, in this study, C, S and O isotopes were combined for the first time to assess pollutant sources on black crust samples: this multi-stable isotope approach allowed to show that the BC formation on monuments from the Monumental Cemetery of Milan mostly results from anthropogenic emissions from fossil fuels combustion by road vehicles and factories, as well as domestic heating.

Statistical Assessment of Water Quality Parameters for Pollution Source Identification in Deepor Beel Area, Guwahati, Assam

Statistical Assessment of Water Quality Parameters for Pollution Source Identification in Deepor Beel Area, Guwahati, Assam

Diagnostic ratios of polyaromatic hydrocarbons for the identification of pollution sources in clams from Okwagbe, Delta State Nigeria

Diagnostic ratios of polyaromatic hydrocarbons for the identification of pollution sources in clams from Okwagbe, Delta State Nigeria

Tracing phosphorus sources in the river-lake system using the oxygen isotope of phosphate

The biogeochemical cycling of phosphorus (P) in river-lake systems presents significant challenges in tracing P sources, highlighting the importance of effective traceability approaches for formulating targeted management measures to mitigate lake eutrophication. In this study, we used the oxygen isotope of phosphate (δ18Op) as a tracer in the river-lake systems, establishing a tracing pathway from potential end-members, through inflow rivers, and eventually to the lake. Taking Dianshan Lake and its main inflow rivers as the study area, we measured δ18Op values of potential end-members, including domestic sewage treatment plant effluents, industrial effluents from phosphorus-related enterprises (printing and dyeing, electroplating, plastics, etc.), and farmland soils. Notably, the industrial effluent signatures ranged from 13.1 ‰ to 21.0 ‰ with an average of 16.8 ‰ ± 3.2 ‰, enriching the δ18Op threshold database. Using the MixSIAR model, it was found that phosphorus in the Jishuigang River primarily originated from agricultural non-point sources and domestic sewage in the dry season, while the Qiandengpu River, with a higher proportion of urban area, had a greater influence from domestic sewage and industrial effluents. Moreover, significant differences were observed between δ18Op values at the lake entrances of the inflow rivers (13.7 ‰ ± 1.0 ‰) and in acid-soluble phosphate of the lake sediments (9.9 ‰ ± 1.0 ‰). Isotopic tracing revealed that phosphorus in the lake originated from both external inputs (80.6 %) and internal release (19.4 %) in the dry season. Alongside pollutant flux calculations based on the hydrological conditions and water quality of the inflow rivers, our findings indicated that phosphorus in Dianshan Lake was mainly attributed to agricultural non-point sources, domestic sewage and sediment release in the dry season. This study provided novel insights into the identification of pollution sources in the river-lake systems, with broad implications for pollution control and environmental protection.

The oxidative potential of fine ambient particulate matter in Xinxiang, North China: Pollution characteristics, source identification and regional transport

Atmospheric fine particulate matter (PM2.5) can trigger the production of cytotoxic reactive oxygen species (ROS), which can trigger or exacerbate oxidative stress and pulmonary inflammation. We collected 111 daily (∼24 h) ambient PM2.5 samples within an urban region of North China during four seasons of 2019–2020. PM2.5 samples were examined for carbonaceous components, water–soluble ions, and elements, together with their oxidative potential (represent ROS–producing ability) by DTT assay. The seasonal peak DTTv was recorded in winter (2.86 ± 1.26 nmol min−1 m−3), whereas the DTTm was the highest in summer (40.6 ± 8.7 pmol min−1 μg−1). WSOC displayed the highest correlation with DTT activity (r = 0.84, p < 0.0001), but the influence of WSOC on the elevation of DTTv was extremely negligible. Combustion source exhibited the most significant and robust correlation with the elevation of DTTv according to the linear mixed–effects model result. Source identification investigation using positive matrix factorization displayed that combustion source (36.2%), traffic source (30.7%), secondary aerosol (15.7%), and dust (14.1%) were driving the DTTv, which were similar to the results from the multiple linear regression (MLR) analysis. Backward trajectory analysis revealed that the major air masses originate from local and regional transportation, but PM2.5 OP was more susceptible to the influence of short–distance transport clusters. Discerning the influence of chemicals on health–pertinent attributes of PM2.5, such as OP, could facilitate a deep understanding of the cause–and–effect relationship between PM2.5 and impacts.

Machine learning approaches to identify spatial factors and their influential distances for heavy metal contamination in downstream sediment

Contaminated sediments can adversely affect aquatic ecosystems, making the identification and management of pollutant sources extremely important. In this study, we proposed machine learning approaches to reveal sources and their influential distances for heavy metal contamination of downstream sediment. We employed classification models with artificial neural networks (ANN) and random forest (RF), respectively, to predict the heavy metal contamination of stream sediments using upland environmental variables as input features. A comprehensive Korean nationwide monitoring database containing 1546 datasets was used to train and test the models. These datasets encompass the concentrations of eight heavy metals (Ar, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) in sediment samples collected from 160 stream sites across the nation from 2014 to 2018. Model's prediction accuracy was evaluated for input feature sets from different influential upland areas defined by different buffer radii and the watershed boundary for each site. Although both ANN and RF models were unsatisfactory in predicting heavy metal quartile classes, RF-classifiers with adaptive synthetic oversampling (ORFC) showed reasonably well-predicted classes of the sediment samples based on the Canada's Sediment Quality Guidelines (accuracy ranged from 0.67 to 0.94). The best influential distance (i.e., buffer radius) was determined for each heavy metal based on the accuracy of ORFC. The results indicated that Cd, Cu and Pb had shorter influential distances (1.5–2.0 km) than the other heavy metals with little difference in accuracy for different influential distances. Feature importance calculation revealed that upland soil contamination was the primary factor for Hg and Ni, while residential areas and roads were significant features associated with Pb and Zn contamination. This approach offers information on major contamination sources and their influential areas to be prioritized for managing contaminated stream sediments.

Machine learning-driven source identification and ecological risk prediction of heavy metal pollution in cultivated soils

To overcome challenges in assessing the impact of environmental factors on heavy metal accumulation in soil due to limited comprehensive data, our study in Yangxin County, Hubei Province, China, analyzed 577 soil samples in combination with extensive big data. We used machine learning techniques, the potential ecological risk index, and the bivariate local Moran’s index (BLMI) to predict Cr, Pb, Cd, As, and Hg concentrations in cultivated soil to assess ecological risks and identify pollution sources. The random forest model was selected for its superior performance among various machine learning models, and results indicated that heavy metal accumulation was substantially influenced by environmental factors such as climate, elevation, industrial activities, soil properties, railways, and population. Our ecological risk assessment highlighted areas of concern, where Cd and Hg were identified as the primary threats. BLMI was used to analyze spatial clustering and autocorrelation patterns between ecological risk and environmental factors, pinpointing areas that require targeted interventions. Additionally, redundancy analysis revealed the dynamics of heavy metal transfer to crops. This detailed approach mapped the spatial distribution of heavy metals, highlighted the ecological risks, identified their sources, and provided essential data for effective land management and pollution mitigation.

Sources, bioaccessibility and health risk of heavy metal(loid)s in the particulate matter of urban areas

Exposure to heavy metal(loid)s in airborne particulate matter (PM) could lead to various adverse health effects. The study investigated the total contents and the bioaccessibility of PM-bound heavy metal(loid)s (Cr, Mn, Co, Ni, Cu, Zn, As, Cd, and Pb), identified their potential sources, and evaluated the associated health risk via inhalation in eight typical cities in China (Nanjing, Mianyang, Huangshi, Nanchang, Kunming, Xiamen, Guangzhou, and Wuzhishan). The results showed that PM-bound Cr (VI) and As of all eight cities exceeded the limits of World Health Organization. The bioaccessibility of PM-bound heavy metal(loid)s exhibited large variations, with their means following the order of Cd > Mn > Co > Ni > Cu > Cr > As > Zn > Pb. Traffic and industrial emissions were identified as primary sources in most urban areas. The emission sources have important effects on the bioaccessibility of PM-bound heavy metal(loid)s. In particular, atmospheric Cu has its bioaccessibility significantly correlated with the contributions from traffic emissions. The bioaccessibility-based health risk assessment obtained different results from those using total contents, showing that the non-carcinogenic risks posed by most metal(loid)s were acceptable except for As in Huangshi and Nanchang. These findings highlight the source dependence of bioaccessibility of heavy metal(loid)s in airborne PM, facilitate the identification of priority pollution sources and enhance effective risk-oriented source regulatory strategies in urban areas.

Identification of groundwater pollution sources based on optimal layout of groundwater pollution monitoring network

Identification of groundwater pollution sources based on optimal layout of groundwater pollution monitoring network

Carbon based sensors for air quality monitoring networks; middle east perspective.

IoT-based Sensors networks play a pivotal role in improving air quality monitoring in the Middle East. They provide real-time data, enabling precise tracking of pollution trends, informed decision-making, and increased public awareness. Air quality and dust pollution in the Middle East region may leads to various health issues, particularly among vulnerable populations. IoT-based Sensors networks help mitigate health risks by offering timely and accurate air quality data. Air pollution affects not only human health but also the region's ecosystems and contributes to climate change. The economic implications of deteriorated air quality include healthcare costs and decreased productivity, underscore the need for effective monitoring and mitigation. IoT-based data can guide policymakers to align with Sustainable Development Goals (SDGs) related to health, clean water, and climate action. The conventional monitor based standard air quality instruments provide limited spatial coverage so there is strong need to continue research integrated with low-cost sensor technologies to make air quality monitoring more accessible, even in resource-constrained regions. IoT-based Sensors networks monitoring helps in understanding these environmental impacts. Among these IoT-based Sensors networks, sensors are of vital importance. With the evolution of sensors technologies, different types of sensors materials are available. Among this carbon based sensors are widely used for air quality monitoring. Carbon nanomaterial-based sensors (CNS) and carbon nanotubes (CNTs) as adsorbents exhibit unique capabilities in the measurement of air pollutants. These sensors are used to detect gaseous pollutants that includes oxides of nitrogen and Sulphur, and ozone, and volatile organic compounds (VOCs). This study provides comprehensive review of integration of carbon nanomaterials based sensors in IoT based network for better air quality monitoring and exploring the potential of machine learning and artificial intelligence for advanced data analysis, pollution source identification, integration of satellite and ground-based networks and future forecasting to design effective mitigation strategies. By prioritizing these recommendations, the Middle East and other regions, can further leverage IoT-based systems to improve air quality monitoring, safeguard public health, protect the environment, and contribute to sustainable development in the region.