In March of 2023, the first National Primary Drinking Water Standards for per- and polyfluoroalkyl substances (PFAS) were announced. The fourth Regulatory Determination that led to this development also included several other contaminants for consideration: 1,4-dioxane (dioxane), 1,2,3-trichloropropane (TCP), and strontium, which faced no determination at this time. In this study, the relative risks associated with these three contaminants and the two regulated PFAS, perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA), are analyzed on a subregion level, considering socioeconomic and racial factors in national exposure and risk levels for the U.S. population. Results indicate that PFOS and PFOA represent the greatest risk to the population in the subregions in which they are detected. Considering race and ethnicity, living in a majority-minority community may be a risk factor for exposure to strontium, while minority status did not increase exposure risk for dioxane, TCP, PFOS, and PFOA. Additionally, total cancer and non-cancer relative health indicator (RHI) matrices indicate that majority-minority communities face significantly greater risks from strontium exposure. Regression models also confirm results for strontium but place the risk on racial/ethnic minority populations more specifically in regions with greater Hispanic/Latino community percentages. Finally, while greater poverty in a subregion is associated with significantly higher cancer and non-cancer RHI values for dioxane, strontium, and TCP, after controlling for state-level variations, multi-level models reveal that greater poverty is associated with significantly lower risk from these three contaminants.
Urban waterlogging has become a critical environmental issue of global significance, frequently causing extensive property damage and human casualties. In Agartala city, severe waterlogging problems arise from rapid urban development, disruption of natural drainage systems, intense rainfall, population growth, and poorly planned drainage infrastructure. With the use of the Analytical Hierarchy Process (AHP) technique, this study attempts to model and identify the hazards, vulnerabilities, and waterlogging risks in Agartala. Waterlogging is particularly prevalent during the monsoon season, when heavy rainfall inundates low-lying areas, leading to significant disruptions. To produce a waterlogging inventory map for the city of Agartala, the primary investigation was carried out. The study employs an integrated approach combining geographic information system (GIS) and remote sensing (RS) techniques, considering 16 parameters to develop hazard and vulnerability maps. The results reveal that approximately 3.45% of Agartala, covering 2.64 sq/km, is in the very high-risk waterlogging zone, while 5.50% (4.21 sq/km) is in the high-risk zone. An additional 18.87% (14.44/km) falls into the moderate-risk category. The remaining areas are classified as low-risk, comprising 36.06 sq/km (47.12%), and very low-risk zones, covering 19.17 sq/km (25.06%). High-risk zones are primarily located in the city’s central part, where low-lying terrain and dense urbanization create conditions conducive to waterlogging. There is a good correlation between the detected waterlogging-prone locations and ground truth data, as evidenced by the receiver operating characteristic (ROC) curve, which produced an area under the curve (AUC) value of 0.801 or 80.1%. This study introduces an innovative approach to assessing waterlogging risk zones, applied for the first time in Agartala city. By developing a comprehensive waterlogging inventory map, the research offers a detailed analysis of spatial variations in waterlogging risk. The study’s findings will assist decision-makers in developing medium- to long-term mitigation strategies to reduce waterlogging-related hazards and guide proper future land-use planning. This research shows that the integrated AHP approach is effective in identifying waterlogging risk zones in Agartala and can support planning and mitigation efforts to prevent future waterlogging incidents globally.
Multi-isotopes can be effectively utilized to offer new insights into heavy-metal oxidation dynamics and variations in redox conditions. Therefore, hydrochemical data and isotopic characteristics (δ18OH2O, δD, δ34SSO4, δ18OSO4, δ15NNO3, δ18ONO3, δ13CDOC and δ13CDIC) were determined the oxidation mechanism of Sb(III) to Sb(V) in D3x4 groundwater. The results showed the concentration of Sb in D3x4 groundwater ranges from 0.005 to 20.700 mg/L, with an average of 2.300 mg/L, and Sb(V) represented the dominant form present within D3x4 groundwater. The δ34S、δ15N values in D3x4 groundwater ranges from -4.20‰ to 6.30‰, 1.20‰ to 22.70‰, respectively. the δ13CDOC and δ13CDIC content in D3x4 groundwater vary in the ranges of -26.97‰ to -16.70‰ and -17.84‰ to -2.30‰, respectively. Stibnite oxidation significantly influenced the enrichment of Sb(V) and SO42−, while microbial nitrification notably contributed to elevated NO3− levels in high-Sb groundwater by converting Sb(III) to Sb(V). The presence of redox-active moieties in DOM facilitated electron transfer for promoting Sb(III) oxidation rate during the stibnite oxidation process. Additionally, microbial oxidative degradation of DOM can promote Sb(V) enrichment, with carbon serving as an energy source for nitrification, facilitated this process and enhances the oxidation rate of Sb(III) to Sb(V). These findings contribute to a more comprehensive understanding of the geochemical behavior of antimony in groundwater and enhance our knowledge regarding Sb(III) oxidation mechanism in oxygenated groundwater.
Lahore (31.320°N; 74.220°E), Pakistan’s second-largest city with a population of 13 million, is considered among the most polluted cities in the world and the most polluted city in Pakistan. We estimated emissions from one of the major sources (brick kilns) and analysed the corresponding impacts on regional air quality. The distribution of pollutants from the brick kilns in Lahore was calculated via a steady-state model (Sutton’s model) and a dynamic model (Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model version 4). Sutton’s model was used to determine the dispersion of a hypothetical pollutant in the vicinity of multiple brick kilns in a steady state. HYSPLIT was employed to compute the dynamic distribution of pollutant concentrations (24 h) for six pollutants using meteorological data (GDAS) and published emission rates of pollutants. The results for November–February 2021–2022 indicated that only SO2 exceeded all the WHO, NEQS, and NAAQS standards in the vicinity of the kilns. A comparison of the modeled concentrations from brick kiln emissions in the middle of Lahore with air pollution data for that area indicated significantly higher pollution levels derived from other pollution sources. It was concluded that in addition to SO2, brick kilns do not contribute significantly to air pollution in a substantial part of the city. However, as the city expands towards the areas where most of the brick kilns are located, this situation will change. These findings underscore the need for strategic urban planning and cleaner kiln technologies to mitigate future air quality impacts.
Predicting the content of heavy metals, such as copper (Cu) and zinc (Zn), based on spectral modeling through Vis–NIR-SWIR spectroscopy is a challenging task, especially when the database comprises soil samples with high variations in Cu and Zn content, associated with pedological, geologic and climate diversity. The aim of this study was to assess whether DB-global stratification, which is based on physiographic region criteria, can improve the accuracy of stratified regional models to predict soil available Cu and Zn content, in comparison with nonstratified global models (GM). Furthermore, we tested the applicability of regional models (RMs) for accurately estimating Cu and Zn in vineyard soils in southern Brazil in comparison with global models. We used a DB-Global model with 1,454 samples derived from 3 different physiographic regions in Rio Grande do Sul State, Brazil. The prediction models were developed via random forest models with spectra subjected to smoothing based on Savitzky-Golay’s 1st derivative. DB-Global stratification based on physiographic regions has shown that grouping the most homogeneous samples increases the prediction accuracy of regional models when they are applied to samples from the specific regions for which they are calibrated. The most accurate predictions were recorded for models calibrated with data in databases with the largest number of samples and with the lowest standard deviations of the Cu, Zn, organic matter and soil clay content. The definition for the calibration and application of a GM, in comparison with an RM, must consider soil pedological diversity in a given region.
Riparian plants exhibit strong antioxidant capacity due to the constant periodic flooding and the resulted oxidative stress. The aim of this study was to determine whether the endophytes are involved in oxidative stress pathway of the host. In the study, we isolated the endophytic fungi from a shrub of Salix variegate before and after natural flooding, and characterized through taxonomical characterization of 18S ITS sequences. By means of total antioxidant capacity (TAC) method, we assessed the antioxidant activity of all isolates. Under different oxygen supply levels, a total of 115 culturable fungi were obtained from various tissues, grouped into 6 classes and 26 genera, showing abundant biodiversity. Aspergillus spp. and Penicillium spp. constituted the dominant population. However, the endophyte community was significantly affected by flooding stress. The fungi in post-flooding population were more numerous and biodiverse, especially the genus Aspergillus. The dominant genera had relatively higher activity than others whether in means or maxima, especially in the genera of Aspergillus after flooding. Our results indicated that flooding would change the population composition of endophyte strains with high antioxidant activity and enhance the antioxidant capacity of Aspergillus, which maybe conversely participate the oxidative pathway in the host.
The global proliferation of Pinus species poses significant threats to biodiversity, ecosystem functioning, and environmental stability. Pinus roxburghii, in particular, has demonstrated a strong potential to encroach upon the indigenous biodiversity of the Himalayan Biodiversity Hotspot (HBH), an area already vulnerable to the impacts of climate change. This study utilized the MaxEnt model, chosen for its robust performance in species distribution modelling, to predict the geographical distribution and actual extent of P. roxburghii for the period 2001–2021 and project its future expansion under two shared socioeconomic pathways (SSP-126 and SSP-585) for 2050 and 2070. The model high predictive accuracy (AUC > 0.9) and metrics (κ and TSS > 0.7) demonstrate its reliability and strong performance. The results reveal a notable expansion of P. roxburghii across the HBH, with a 1.61% spatio-temporal increase (11,142.16 km2) and a 0.65% rise in habitat suitability (4478.47 km2) under future scenarios. Key bioclimatic variables influencing its distribution include BIO6 (minimum temperature of the coldest month) and BIO17 (precipitation of the driest quarter), contributing 69.54% and 85.28% to the model under current and future scenarios, respectively. These findings highlight the urgent need for targeted adaptive management strategies, such as early detection systems and habitat restoration initiatives, to mitigate the encroachment of P. roxburghii and safeguard native biodiversity.
The Akaki River, in Ethiopia, becomes a source of antimicrobial-resistant (AMR) pathogens and genes that are spreading to receiving water. Water quality monitoring (WQM) is limited in Akaki, and the available evidence is based on short-term monitoring of inconsistent sampling sites and water quality parameters. Therefore, we designed a suitable WQM plan for the Big Akaki River receiving wastewater from rural, urban, and peri-urban areas. WQM plan was designed by employing multiple approaches including literature review, field observations, spatial analysis, and pollutant “hotspot” identification. Information was extracted through a systematic review of 48 articles, selected through a screening process, to guide the selection of suitable monitoring sites. Field observation was used to inspect previously sampled sites and identify pollution sources and exposure routes to antibiotic-resistant bacteria and zoonotic pathogens. For validation, water samples were collected from 40 sites identified from the literature review and field observation, and results were refined during a stakeholder consultation workshop. Hotspots were identified based on chemical oxygen demand, dissolved oxygen, ammonia, and extended-spectrum βeta-lactamase (ESβL)-producing Escherichia coli and Salmonella enteritidis/Shigella flexneri data. Cluster analysis of the water quality data categorized the 40 sites into three groups, and the number of sites for future monitoring to 20, including possible pollutant hotspots, reference sites, known pollution sources, exposure routes, and availability of river discharge data. The WQM plan will help AMR and zoonotic pathogens monitoring and mitigation in the study sites. Our approach can be replicated to design WQM plans for other rivers.
Liquid olive waste presents one of the major problems for the environment in olive oil-producing countries. The goal is to identify the crushing method that decreases the environmental impact, particularly in terms of the organic loading and microbial contamination to guide the future treatment and recovery strategies. The results obtained from the chemical oxygen demand (COD) have shown that the continuous three-phase system exhibited the highest COD value, reaching 31 × 104 mg O2/l, and a concentration of 13 × 103 mg/l for polyphenols and 2137.4 mg/l for hydroxytyrosol. The microbiological study is yeasts, fungi, and bacteria in the three methods. The two-phase method had the highest microbial load with great concentrations of nitrogenous compounds. As a result of the preservation of our environment, we see that the two-phase method represents the least polluting method, compared to the other methods.