The source characterization of nitrate (NO) in groundwater of Muling-Xingkai Plain (MXP) and the influence of NO on the water environment were studied by hydrogeochemical and multivariate statistical analysis. A total of 164 groundwater samples were collected, and the samples were classified into three clusters by using hierarchical cluster analysis. Cluster 1 (C1), accounting for 13% of total samples, was mainly located in local residential zones where the top soils were the medium-textured sediments. Cluster 2 (C2) and cluster 3 (C3) were mainly located in farmlands and residential zones where the clay sediments were overlaying the aquifers. The soil media covering the aquifers was an important factor controlling the concentration of NO in groundwater, which determined the infiltration rate of wastewater and the redox environment of aquifers. Only the samples in C1 exceeded the WTO standards for NO (50 mg/L), and the samples in C2 and C3 had low NO concentration (less than 10 mg/L). The excessive NO in groundwater was observed in the shallow groundwater under local residential zones, and it was closely related to the anthropogenic activities since the 1950s. The domestic sewage was responsible for the elevated NO contents in the MXP. Then, it was still necessary to construct the sewage disposal system in rural areas to further protect the groundwater resource to avoid the formation of extensive nitrogen pollution. At present, NO in the groundwater mainly shows a fertilizer and natural rainwater origin and is not demonstrating the significant deterioration of groundwater qualities and water environment.
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http://dx.doi.org/10.1007/s10661-020-08347-6 | DOI Listing |
Heliyon
January 2025
Environmental Energy Technologies Laboratory (EETL), Department of Physics, University of Yaounde I, P.O Box 812 Yaounde, Cameroon.
This article analyzes and compares three methodologies for identifying suitable regions for solar hydrogen production using photovoltaic panels: AHP (Analytic Hierarchy Process), FAHP (Fuzzy Analytic Hierarchy Process), and MC-FAHP (Monte Carlo FAHP), integrated with GIS (Geographic Information Systems). The study employs ten criteria across technical (Global Horizontal Irradiance, temperature, slope, elevation, orientation), economic (distance from transportation and electrical networks), and social (population density, proximity to residential areas) factors. Environmental and exclusion criteria define restrictive zones.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA 02114.
Radon, a common radioactive indoor air pollutant, is the second leading cause of lung cancer in the United States. Knowledge about its distribution is essential for risk assessment and designing efficient protective regulations. However, the three current radon maps for the United States are unable to provide the up-to-date, high-resolution, and time-varying radon concentrations.
View Article and Find Full Text PDFPLoS One
January 2025
Escuela de Ingeniería Química, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile.
In this comprehensive analysis of Chile's air quality dynamics spanning 2016 to 2021, the utilization of data from the National Air Quality Information System (SINCA) and its network of monitoring stations was undertaken. Quintero, Puchuncaví, and Coyhaique were the focal points of this study, with the primary objective being the construction of predictive models for sulfur dioxide (SO2), fine particulate matter (PM2.5), and coarse particulate matter (PM10).
View Article and Find Full Text PDFEnviron Res
January 2025
Department of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China. Electronic address:
Background: Exposure to residential greenness has been linked with improved sleep duration; however, longitudinal evidence is limited, and the potential mediating effect of ambient fine particulate matter (PM) has yet to be assessed.
Methods: We obtained data for 19,567 participants across seven counties in a prospective cohort in Ningbo, China. Greenness was estimated using Normalized Difference Vegetation Index (NDVI) within 250-m, 500-m and 1000-m buffer zones, while yearly average PM concentrations were measured using validated land-use regression models, both based on individual residential addresses.
Biology (Basel)
December 2024
Federal Research Center "Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences", 142290 Pushchino, Russia.
Urban dust samples were collected in Moscow (Russia) in June 2021. The samples were collected in three functional zones of Moscow (traffic, residential, and recreational) and included air microparticles, leaf dust, and paved dust. Data on the taxonomic composition of bacterial communities were obtained for dust samples, and their functional characteristics were predicted using PICRUSt2 2.
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