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1.
Sci Rep ; 11(1): 4285, 2021 02 19.
Artigo em Inglês | MEDLINE | ID: mdl-33608603

RESUMO

On January 30, 2020, India recorded its first COVID-19 positive case in Kerala, which was followed by a nationwide lockdown extended in four different phases from 25th March to 31st May, 2020, and an unlock period thereafter. The lockdown has led to colossal economic loss to India; however, it has come as a respite to the environment. Utilizing the air quality index (AQI) data recorded during this adverse time, the present study is undertaken to assess the impact of lockdown on the air quality of Ankleshwar and Vapi, Gujarat, India. The AQI data obtained from the Central Pollution Control Board was assessed for four lockdown phases. We compared air quality data for the unlock phase with a coinciding period in 2019 to determine the changes in pollutant concentrations during the lockdown, analyzing daily AQI data for six pollutants (PM10, PM2.5, CO, NO2, O3, and SO2). A meta-analysis of continuous data was performed to determine the mean and standard deviation of each lockdown phase, and their differences were computed in percentage in comparison to 2019; along with the linear correlation analysis and linear regression analysis to determine the relationship among the air pollutants and their trend for the lockdown days. The results revealed different patterns of gradual to a rapid reduction in most of the pollutant concentrations (PM10, PM2.5, CO, SO2), and an increment in ozone concentration was observed due to a drastic reduction in NO2 by 80.18%. Later, increases in other pollutants were also observed as the restrictions were eased during phase-4 and unlock 1. The comparison between the two cities found that factors like distance from the Arabian coast and different industrial setups played a vital role in different emission trends.


Assuntos
Poluição do Ar/estatística & dados numéricos , Controle de Doenças Transmissíveis/normas , Monitoramento Ambiental/estatística & dados numéricos , Poluentes Atmosféricos/análise , Poluição do Ar/análise , /transmissão , Cidades/estatística & dados numéricos , Humanos , Índia , Indústrias/normas , Material Particulado/análise
2.
PLoS One ; 15(12): e0243734, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33370322

RESUMO

The cycling and fate of polycyclic aromatic hydrocarbons (PAHs) is not well understood in estuarine systems. It is critical now more than ever given the increased ecosystem pressures on these critical coastal habitats. A budget of PAHs and cycling has been created for Galveston Bay (Texas) in the northwestern Gulf of Mexico, an estuary surrounded by 30-50% of the US capacity of oil refineries and chemical industry. We estimate that approximately 3 to 4 mt per year of pyrogenic PAHs are introduced to Galveston Bay via gaseous exchange from the atmosphere (ca. 2 mt/year) in addition to numerous spills of petrogenic PAHs from oil and gas operations (ca. 1.0 to 1.9 mt/year). PAHs are cycled through and stored in the biota, and ca. 20 to 30% of the total (0.8 to 1.5 mt per year) are estimated to be buried in the sediments. Oysters concentrate PAHs to levels above their surroundings (water and sediments) and contain substantially greater concentrations than other fish catch (shrimp, blue crabs and fin fish). Smaller organisms (infaunal invertebrates, phytoplankton and zooplankton) might also retain a significant fraction of the total, but direct evidence for this is lacking. The amount of PAHs delivered to humans in seafood, based on reported landings, is trivially small compared to the total inputs, sediment accumulation and other possible fates (metabolic remineralization, export in tides, etc.), which remain poorly known. The generally higher concentrations in biota from Galveston Bay compared to other coastal habitats can be attributed to both intermittent spills of gas and oil and the bay's close proximity to high production of pyrogenic PAHs within the urban industrial complex of the city of Houston as well as periodic flood events that transport PAHs from land surfaces to the Bay.


Assuntos
Baías/química , Monitoramento Ambiental/estatística & dados numéricos , Hidrocarbonetos Policíclicos Aromáticos/metabolismo , Poluentes Químicos da Água/metabolismo , Animais , Organismos Aquáticos/química , Organismos Aquáticos/metabolismo , Atmosfera/química , Braquiúros/química , Braquiúros/metabolismo , Peixes/metabolismo , Sedimentos Geológicos/química , Golfo do México , Ostreidae/química , Ostreidae/metabolismo , Poluição por Petróleo/estatística & dados numéricos , Texas , Poluentes Químicos da Água/análise
3.
PLoS One ; 15(9): e0238422, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32960894

RESUMO

Streams and rivers are biodiverse and provide valuable ecosystem services. Maintaining these ecosystems is an important task, so organisations often monitor the status and trends in stream condition and biodiversity using field sampling and, more recently, autonomous in-situ sensors. However, data collection is often costly, so effective and efficient survey designs are crucial to maximise information while minimising costs. Geostatistics and optimal and adaptive design theory can be used to optimise the placement of sampling sites in freshwater studies and aquatic monitoring programs. Geostatistical modelling and experimental design on stream networks pose statistical challenges due to the branching structure of the network, flow connectivity and directionality, and differences in flow volume. Geostatistical models for stream network data and their unique features already exist. Some basic theory for experimental design in stream environments has also previously been described. However, open source software that makes these design methods available for aquatic scientists does not yet exist. To address this need, we present SSNdesign, an R package for solving optimal and adaptive design problems on stream networks that integrates with existing open-source software. We demonstrate the mathematical foundations of our approach, and illustrate the functionality of SSNdesign using two case studies involving real data from Queensland, Australia. In both case studies we demonstrate that the optimal or adaptive designs outperform random and spatially balanced survey designs implemented in existing open-source software packages. The SSNdesign package has the potential to boost the efficiency of freshwater monitoring efforts and provide much-needed information for freshwater conservation and management.


Assuntos
Ecossistema , Monitoramento Ambiental/métodos , Rios , Software , Teorema de Bayes , Biodiversidade , Monitoramento Ambiental/estatística & dados numéricos , Modelos Estatísticos , Queensland
4.
PLoS One ; 15(8): e0237971, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32833977

RESUMO

Satellite-based methods are proposed for the estimation of clear day average hourly illuminance from satellite data under local climate conditions. First, aerosol optical depth (AOD) data collected using a ground-based sun photometer were used to calibrate the satellite remote sensing AOD data. Next, we screened for the factors affecting the illuminance of clear sky and detected three important factors, namely the sine of the solar altitude angle, aerosol optical thickness, and atmospheric precise water content. Finally, based on the AOD data of satellite remote sensing, combined with the local illumination data and meteorological data, a clear sky average hourly illumination model in Chongqing was established via the regression method. There was good agreement between the calculated and the measured values of clear day average hourly illuminance, with a root mean square difference and mean bias difference of 22% and -0.05%, respectively. The model was used to map clear day annual, quarterly, and monthly average hourly illuminance. The maps show the clear day annual, seasonal, and monthly variations of average hourly illuminance in Chongqing.


Assuntos
Poluição do Ar , Monitoramento Ambiental/estatística & dados numéricos , Luz , Fenômenos Ópticos , China , Cidades , Modelos Estatísticos , Estações do Ano , Astronave
5.
PLoS One ; 15(8): e0238165, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32841269

RESUMO

Vegetation mapping is of considerable significance to both geoscience and mountain ecology, and the improved resolution of remote sensing images makes it possible to map vegetation at a finer scale. While the automatic classification of vegetation has gradually become a research hotspot, real-time and rapid collection of samples has become a bottleneck. How to achieve fine-scale classification and automatic sample selection at the same time needs further study. Stratified sampling based on appropriate prior knowledge is an effective sampling method for geospatial objects. Therefore, based on the idea of stratified sampling, this paper used the following three steps to realize the automatic selection of representative samples and classification of fine-scale mountain vegetation: 1) using Mountain Altitudinal Belt (MAB) distribution information to stratify the study area into multiple vegetation belts; 2) selecting and correcting samples through iterative clustering at each belt automatically; 3) using RF (Random Forest) classifier with strong robustness to achieve automatic classification. The average sample accuracy of nine vegetation formations was 0.933, and the total accuracy of the classification result was 92.2%, with the kappa coefficient of 0.910. The results showed that this method could automatically select high-quality samples and obtain a high-accuracy vegetation map. Compared with the traditional vegetation mapping method, this method greatly improved the efficiency, which is of great significance for the fine-scale mountain vegetation mapping in large-scale areas.


Assuntos
Altitude , Ecossistema , Plantas/classificação , Imagens de Satélites , Algoritmos , China , Análise por Conglomerados , Bases de Dados Factuais , Monitoramento Ambiental/estatística & dados numéricos , Processamento de Imagem Assistida por Computador , Redes Neurais de Computação , Tecnologia de Sensoriamento Remoto/estatística & dados numéricos , Imagens de Satélites/estatística & dados numéricos
6.
PLoS One ; 15(8): e0237325, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32810134

RESUMO

Recent research has shown relationships between health outcomes and residence proximity to unconventional oil and natural gas development (UOGD). The challenge of connecting health outcomes to environmental stressors requires ongoing research with new methodological approaches. We investigated UOGD density and well emissions and their association with symptom reporting by residents of southwest Pennsylvania. A retrospective analysis was conducted on 104 unique, de-identified health assessments completed from 2012-2017 by residents living in proximity to UOGD. A novel approach to comparing estimates of exposure was taken. Generalized linear modeling was used to ascertain the relationship between symptom counts and estimated UOGD exposure, while Threshold Indicator Taxa Analysis (TITAN) was used to identify associations between individual symptoms and estimated UOGD exposure. We used three estimates of exposure: cumulative well density (CWD), inverse distance weighting (IDW) of wells, and annual emission concentrations (AEC) from wells within 5 km of respondents' homes. Taking well emissions reported to the Pennsylvania Department of Environmental Protection, an air dispersion and screening model was used to estimate an emissions concentration at residences. When controlling for age, sex, and smoker status, each exposure estimate predicted total number of reported symptoms (CWD, p<0.001; IDW, p<0.001; AEC, p<0.05). Akaike information criterion values revealed that CWD was the better predictor of adverse health symptoms in our sample. Two groups of symptoms (i.e., eyes, ears, nose, throat; neurological and muscular) constituted 50% of reported symptoms across exposures, suggesting these groupings of symptoms may be more likely reported by respondents when UOGD intensity increases. Our results do not confirm that UOGD was the direct cause of the reported symptoms but raise concern about the growing number of wells around residential areas. Our approach presents a novel method of quantifying exposures and relating them to reported health symptoms.


Assuntos
Poluentes Atmosféricos/efeitos adversos , Exposição Ambiental/efeitos adversos , Fraturamento Hidráulico , Gás Natural/efeitos adversos , Campos de Petróleo e Gás , Adulto , Monitoramento Ambiental/estatística & dados numéricos , Feminino , Humanos , Modelos Lineares , Masculino , Pessoa de Meia-Idade , Pennsylvania , Estudos Retrospectivos , Níveis Máximos Permitidos
7.
PLoS One ; 15(8): e0237324, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32813701

RESUMO

Flood inundation maps provide valuable information towards flood risk preparedness, management, communication, response, and mitigation at the time of disaster, and can be developed by harnessing the power of satellite imagery. In the present study, Sentinel-1 Synthetic Aperture RADAR (SAR) data and Otsu method were utilized to map flood inundation areas. Google Earth Engine (GEE) was used for implementing Otsu algorithm and processing Sentinel-1 SAR data. The results were assessed by (i) calculating a confusion matrix; (ii) comparing the submerge water areas of flooded (Aug 2018), non-flooded (Jan 2018) and previous year's flooded season (Aug 2016, Aug 2017), and (iii) analyzing historical rainfall patterns to understand the flood event. The overall accuracy for the Sentinel-1 SAR flood inundation maps of 9th and 21st August 2018 was observed as 94.3% and 94.1% respectively. The submerged area (region under water) classified significant flooding as compared to the non-flooded (January 2018) and previous year's same season (August 2015-2017) classified outputs. Summing up, observations from Sentinel-1 SAR data using Otsu algorithm in GEE can act as a powerful tool for mapping flood inundation areas at the time of disaster, and enhance existing efforts towards saving lives and livelihoods of communities, and safeguarding infrastructure and businesses.


Assuntos
Monitoramento Ambiental/métodos , Inundações/estatística & dados numéricos , Imagens de Satélites , Navegador , Monitoramento Ambiental/estatística & dados numéricos , Índia , Rios
8.
PLoS One ; 15(6): e0233297, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32492035

RESUMO

Metal contamination of food and water resources is a known public health issue in Arctic and sub-Arctic communities due to the proximity of many communities to mining and drilling sites. In addition, permafrost thaw may release heavy metals sequestered in previously frozen soils, potentially contaminating food and water resources by increasing the concentration of metals in freshwater, plants, and wildlife. Here we assess the enrichment of selected heavy metals in Alaskan soils by synthesizing publicly available data of soil metal concentrations. We analyzed data of soil concentrations of arsenic, chromium, mercury, nickel, and lead from over 1,000 samples available through the USGS Alaskan Geochemical Database to evaluate 1) the spatial distribution of sampling locations for soil metal analysis, 2) metal concentrations in soils from different land cover types and depths, and 3) the occurrence of soils in Alaska with elevated metal concentrations relative to other soils. We found substantial clustering of sample sites in the southwestern portion of Alaska in discontinuous and sporadic permafrost, while the continuous permafrost zone in Northern Alaska and the more populous Interior are severely understudied. Metal concentration varied by land cover type but lacked consistent patterns. Concentrations of chromium, mercury, and lead were higher in soils below 10 cm depth, however these deeper soils are under-sampled. Arsenic, chromium, mercury, nickel and lead concentrations exceeded average values for US soils by one standard deviation or more in 3.7% to 18.7% of the samples in this dataset. Our analysis highlights critical gaps that impede understanding of how heavy metals in thawing permafrost soils may become mobilized and increase exposure risk for Arctic communities.


Assuntos
Metais Pesados/análise , Poluentes do Solo/análise , Alaska , Regiões Árticas , Arsênico/análise , Cromo/análise , Bases de Dados Factuais/estatística & dados numéricos , Monitoramento Ambiental/estatística & dados numéricos , Geografia , Aquecimento Global , Humanos , Chumbo/análise , Mercúrio/análise , Mineração , Níquel/análise , Pergelissolo/química
9.
Environ Geochem Health ; 42(11): 3795-3810, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-32594417

RESUMO

As the upper reach of the Yangtze River, the Jinsha River has experienced ecological degradation due to increased anthropogenic activities. The potential pollution sources affecting the Jinsha River watershed from 2016 to 2018 were investigated using an improved method in combination with correlation analysis and the absolute principal component score-multiple linear regression receptor modeling technique. Our results identified 5-7 potential pollution sources in the Jinsha main stream watershed and the Pudu, Niulan, and Yalong River watersheds of the Jinsha River. The water pollutant concentrations of the Jinsha main stream watershed were mainly influenced by environmental, agricultural, and human population factors. In the Pudu River watershed, the primary pollution sources changed to natural and sedimentary pollutant sources. It is necessary to control the sedimentary pollutants. The Niulan River watershed was also influenced by natural environment factors. Among those, mineral, sedimentary pollutant, and meteorological sources contributed the most to water quality. In the case of the Yalong River watershed, the influence of non-point source pollution caused by human activities and sedimentary pollutants was the main reason for the deterioration of the ecological environment. The multivariate statistical techniques presented good adaptability for the analysis of pollution sources in the Jinsha River watershed, and the results may be useful for the protection and management of the watershed eco-environment.


Assuntos
Poluentes Químicos da Água/análise , Poluição da Água/análise , Agricultura , China , Monitoramento Ambiental/métodos , Monitoramento Ambiental/estatística & dados numéricos , Sedimentos Geológicos/química , Humanos , Modelos Lineares , Análise Multivariada , Análise de Componente Principal , Rios , Qualidade da Água
10.
BMC Public Health ; 20(1): 1017, 2020 Jun 26.
Artigo em Inglês | MEDLINE | ID: mdl-32590953

RESUMO

BACKGROUND: Quantifying the potential cancer cases associated with environmental carcinogen exposure can help inform efforts to improve population health. This study developed an approach to estimate the environmental burden of cancer and applied it to Ontario, Canada. The purpose was to identify environmental carcinogens with the greatest impact on cancer burden to support evidence-based decision making. METHODS: We conducted a probabilistic assessment of the environmental burden of cancer in Ontario. We selected 23 carcinogens that we defined as "environmental" (e.g., pollutants) and were relevant to the province, based on select classifications provided by the International Agency for Research on Cancer. We evaluated population exposure to the carcinogens through inhalation of indoor/outdoor air; ingestion of food, water, and dust; and exposure to radiation. We obtained or calculated concentration-response functions relating carcinogen exposure and the risk of developing cancer. Using both human health risk assessment and population attributable fraction models in a Monte Carlo simulation, we estimated the annual cancer cases associated with each environmental carcinogen, reporting the simulation summary (e.g., mean and percentiles). RESULTS: We estimated between 3540 and 6510 annual cancer cases attributable to exposure to 23 environmental carcinogens in Ontario. Three carcinogens were responsible for over 90% of the environmental burden of cancer: solar ultraviolet (UV) radiation, radon in homes, and fine particulate matter (PM2.5) in outdoor air. Eight other carcinogens had an estimated mean burden of at least 10 annual cancer cases: acrylamide, arsenic, asbestos, chromium, diesel engine exhaust particulate matter, dioxins, formaldehyde, and second-hand smoke. The remaining 12 carcinogens had an estimated mean burden of less than 10 annual cancer cases in Ontario. CONCLUSIONS: We found the environmental burden of cancer in Ontario to fall between previously estimated burdens of alcohol and tobacco use. These results allow for a comparative assessment across carcinogens and offer insights into strategies to reduce the environmental burden of cancer. Our analysis could be adopted by other jurisdictions and repeated in the future for Ontario to track progress in reducing cancer burden, assess newly classified environmental carcinogens, and identify top burden contributors.


Assuntos
Carcinógenos Ambientais/administração & dosagem , Efeitos Psicossociais da Doença , Exposição Ambiental/efeitos adversos , Monitoramento Ambiental/estatística & dados numéricos , Neoplasias/induzido quimicamente , Asbestos/efeitos adversos , Carcinógenos , Carcinógenos Ambientais/análise , Exposição Ambiental/estatística & dados numéricos , Humanos , Neoplasias/epidemiologia , Ontário , Material Particulado/análise , Medição de Risco , Fatores de Risco
11.
PLoS One ; 15(6): e0234364, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32530933

RESUMO

Inadequate sanitation can lead to exposure to fecal contamination through multiple environmental pathways and can result in adverse health outcomes. By understanding the relative importance of multiple exposure pathways, sanitation interventions can be tailored to those pathways with greatest potential public health impact. The SaniPath Exposure Assessment Tool allows users to identify and quantify human exposure to fecal contamination in low-resource urban settings through a systematic yet customizable process. The Tool includes: a project management platform; mobile data collection and a data repository; protocols for primary data collection; and automated exposure assessment analysis. The data collection protocols detail the process of conducting behavioral surveys with households, school children, and community groups to quantify contact with fecal exposure pathways and of collecting and analyzing environmental samples for E. coli as an indicator of fecal contamination. Bayesian analyses are used to estimate the percentage of the population exposed and the mean dose of fecal exposure from microbiological and behavioral data. Fecal exposure from nine pathways (drinking water, bathing water, surface water, ocean water, open drains, floodwater, raw produce, street food, and public or shared toilets) can be compared through a common metric-estimated ingestion of E. coli units (MPN or CFU) per month. The Tool generates data visualizations and recommendations for interventions designed for both scientific and lay audiences. When piloted in Accra, Ghana, the results of the Tool were comparable with that of an in-depth study conducted in the same neighborhoods and highlighted consumption of raw produce as a dominant exposure pathway. The Tool has been deployed in nine cities to date, and the results are being used by local authorities to design and prioritize programming and policy. The SaniPath Tool is a novel approach to support public-health evidence-based decision-making for urban sanitation policies and investments.


Assuntos
Microbiologia Ambiental , Monitoramento Ambiental/métodos , Fezes/microbiologia , Saneamento/estatística & dados numéricos , Software , Cidades , Tomada de Decisões , Exposição Ambiental , Monitoramento Ambiental/estatística & dados numéricos , Escherichia coli/isolamento & purificação , Contaminação de Alimentos , Gana , Humanos , Projetos Piloto , Formulação de Políticas , Pobreza , Saúde Pública , Saúde da População Urbana , Microbiologia da Água
12.
Chemosphere ; 258: 127285, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-32540537

RESUMO

Many instrumental methods of analysis require the daily collection of calibrator signals to calibrate their response. The quality of quantifications based on these calibrations depends on calibrators quality, instrumental signal performance and regression model fitness. Linear Ordinary Least Squares (LOLS), Linear Weighted Least Squares (LWLS) or Linear Bivariate Least Squares (LBLS) regression models can be used to calibrate and evaluate the uncertainty from instrumental quantifications, but require the fulfilment of some assumptions, namely, constant signal variance (LOLS), high calibrators quality (LOLS and LWLS) and linear variation of instrumental signal with calibrator values. The LBLS is flexible regarding calibrator values uncertainty and correlation but requires the determination of calibrator values and signals covariances. This work developed a computational tool for the bottom-up evaluation of global instrumental quantifications uncertainty which simulates calibrator values correlations from entered calibrators preparation procedure and simulates calibrators and samples signals precision from prior precision data, allowing accurate uncertainty evaluation from a few replicate signals of the daily calibration. The used signal precision models were built from previously observed repeatability variation throughout the calibration interval adjusted to daily precision condition from a residual standard deviation adjustment factor. This approach was implemented in a user-friendly MS-Excel file and was successfully applied to the analysis of As, Cd, Ni and Pb in marine sediment extracts by Absorption Spectroscopy. Evaluations were tested by the metrological compatibility of estimated and reference values of control standards for confidence levels of 95% and 99%. The success rates of the compatibility tests were statistically equivalent to the confidence level (p-value>0.01).


Assuntos
Monitoramento Ambiental/estatística & dados numéricos , Método de Monte Carlo , Incerteza , Poluentes Químicos da Água/análise , Calibragem , Monitoramento Ambiental/métodos , Sedimentos Geológicos/análise , Metais Pesados/análise , Variações Dependentes do Observador , Valores de Referência , Reprodutibilidade dos Testes , Espectroscopia por Absorção de Raios X/métodos , Espectroscopia por Absorção de Raios X/estatística & dados numéricos
13.
J Hosp Infect ; 105(4): 589-592, 2020 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-32590011

RESUMO

The role of the hospital environment in the transmission of infection is well described. With an emerging infection whose mode of transmission is under investigation, strict infection prevention and control measures, including patient isolation, hand hygiene, personal protective equipment that is doffed on exiting the patient room, and environmental cleaning should be implemented to prevent spread. Environmental testing demonstrated that COVID-19 patients contaminated the patient area (11/26, 42.3% of tests) but contamination of general ward areas was minimal (1/30, 3%) and the virus was detected after cleaning on one item only (1/25, 4%) which was noted to be in disrepair.


Assuntos
Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/transmissão , Surtos de Doenças/prevenção & controle , Hospitais/estatística & dados numéricos , Controle de Infecções/métodos , Controle de Infecções/estatística & dados numéricos , Pandemias/prevenção & controle , Quartos de Pacientes/estatística & dados numéricos , Pneumonia Viral/prevenção & controle , Pneumonia Viral/transmissão , Betacoronavirus , Monitoramento Ambiental/métodos , Monitoramento Ambiental/estatística & dados numéricos , Humanos , Irlanda
14.
PLoS One ; 15(4): e0231692, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32294108

RESUMO

Soil erosion is a global environmental threat, and Land Use Land Cover Changes (LUCC) have significant impacts on it. Nepal, being a mountainous country, has significant soil erosion issues. To examine the effects of LUCC on water erosion, we studied the LUCC in Sarada, Rapti and Thuli Bheri river basins of Nepal during the 1995-2015 period using the Remote Sensing. We calculated the average annual soil loss using the Revised Universal Soil Loss Equation and Geographical Information System. Our results suggest that an increase in the agricultural lands at the expense of bare lands and forests escalated the soil erosion through the years; rates being 5.35, 5.47 and 6.03 t/ha/year in 1995, 2007 and 2015, respectively. Of the different land uses, agricultural land experienced the most erosion, whereas the forests experienced the least erosion. Agricultural lands, particularly those on the steeper slopes, were severely degraded and needed urgent soil and water conservation measures. Our study confirms that the long term LUCC has considerable impacts on soil loss, and these results can be implemented in similar river basins in other parts of the country.


Assuntos
Conservação dos Recursos Naturais , Produção Agrícola , Monitoramento Ambiental/estatística & dados numéricos , Solo , Florestas , Sistemas de Informação Geográfica/estatística & dados numéricos , Nepal , Rios
15.
PLoS One ; 15(4): e0231678, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32294128

RESUMO

The southern Gulf of Mexico (sGoM) is home to an extensive oil recovery and development infrastructure. In addition, the basin harbors sites of submarine hydrocarbon seepage and receives terrestrial inputs from bordering rivers. We used stable carbon, nitrogen, and radiocarbon analyses of bulk sediment organic matter to define the current baseline isoscapes of surface sediments in the sGoM and determined which factors might influence them. These baseline surface isoscapes will be useful for accessing future environmental impacts. We also examined the region for influence of hydrocarbon deposition in the sedimentary record that might be associated with hydrocarbon recovery, spillage and seepage, as was found in the northern Gulf of Mexico (nGoM) following the Deepwater Horizon (DWH) oil spill in 2010. In 1979, the sGoM experienced a major oil spill, Ixtoc 1. Surface sediment δ13C values ranged from -22.4‰ to -19.9‰, while Δ14C values ranged from -337.1‰ to -69.2‰. Sediment δ15N values ranged from 2.8‰ to 7.2‰, while the %C on a carbonate-free basis ranged in value of 0.65% to 3.89% and %N ranged in value of 0.09% to 0.49%. Spatial trends for δ13C and Δ14C were driven by water depth and distance from the coastline, while spatial trends for δ15N were driven by location (latitude and longitude). Location and distance from the coastline were significantly correlated with %C and %N. At depth in two of twenty (10%) core profiles, we found negative δ13C and Δ14C excursions from baseline values in bulk sedimentary organic material, consistent with either oil-residue deposition or terrestrial inputs, but likely the latter. We then used 210Pb dating on those two profiles to determine the time in which the excursion-containing horizons were deposited. Despite the large spill in 1979, no evidence of hydrocarbon residue remained in the sediments from this specific time period.


Assuntos
Radioisótopos de Carbono/análise , Monitoramento Ambiental/estatística & dados numéricos , Sedimentos Geológicos/análise , Datação Radiométrica/estatística & dados numéricos , Isótopos de Carbono/análise , Golfo do México , Radioisótopos de Chumbo/análise , Nitrogênio/análise
16.
PLoS One ; 15(4): e0231671, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32302331

RESUMO

Water resources are indispensable for all social-economic activities and ecosystem functions. In addition, changes in water resources have great significance for agricultural production. This paper uses five global climate models from CMIP5 to evaluate the future spatiotemporal variation in water resources in China under four RCP scenarios. The results show that the available precipitation significantly decreases due to evapotranspiration. Comparing the four RCP scenarios, the national average of the available precipitation is the highest under the RCP 2.6 and 4.5 scenarios, followed by that under the RCP 8.5 scenario. In terms of spatial distribution, the amount of available precipitation shows a decreasing trend from southeast to northwest. Regarding temporal changes, the available precipitation under RCP 8.5 exhibits a trend of first increasing and then decreasing, while the available precipitation under the RCP 6.0 scenario exhibits a trend of first decreasing and then increasing. Under the RCP 2.6 and 4.5 scenarios, the available precipitation increases, and the RCP 4.5 scenario has a higher rate of increase than that of RCP 2.6. In the context of climate change, changes in water resources and temperature cause widespread increases in potential agricultural productivity around Hu's line, especially in southwestern China. However, the potential agricultural productivity decreases in a large area of southeastern China. Hu's line has a partial breakthrough in the locking of agriculture, mainly in eastern Tibet, western Sichuan, northern Yunnan and northwestern Inner Mongolia. The results provide a reference for the management and deployment of future water resources and can aid in agricultural production in China.


Assuntos
Agricultura/tendências , Mudança Climática , Previsões , Recursos Hídricos/provisão & distribução , China , Conjuntos de Dados como Assunto , Monitoramento Ambiental/estatística & dados numéricos , Modelos Estatísticos , Chuva , Análise Espaço-Temporal
17.
Am J Psychiatry ; 177(8): 735-743, 2020 08 01.
Artigo em Inglês | MEDLINE | ID: mdl-32312109

RESUMO

OBJECTIVE: Although the association between ambient air pollution and risk of depression has been investigated in several epidemiological studies, the evidence is still lacking for hospital admissions for depression, which indicates a more severe form of depressive episode. The authors used national morbidity data to investigate the association between short-term exposure to ambient air pollution and daily hospital admissions for depression. METHODS: Using data from the Chinese national medical insurance databases for urban populations, the authors conducted a two-stage time-series analysis to investigate the associations of short-term exposure to major ambient air pollutants-fine particles (PM2.5), inhalable particles (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO)-and daily hospital admission risk for depression in 75 Chinese cities during the period 2013-2017. RESULTS: The authors identified 111,620 hospital admissions for depression in 75 cities. In the single-pollutant models, the effect estimates of all included air pollutants, with the exception of O3, were significant at several lags within 7 days. For example, 10 µg/m3 increases in PM2.5, PM10, and NO2 at lag01 were associated with increases of 0.52% (95% CI=0.03, 1.01), 0.41% (95% CI=0.05, 0.78), and 1.78% (95% CI=0.73, 2.83), respectively, in daily hospital admissions for depression. Subgroup, sensitivity, and two-pollutant model analyses highlighted the robustness of the effect estimates for NO2. CONCLUSIONS: The study results suggest that short-term exposure to ambient air pollution is associated with an increased risk of daily hospital admission for depression in the general urban population in China, which may have important implications for improving mental wellness among the public.


Assuntos
Poluentes Atmosféricos , Depressão , Exposição Ambiental , Monitoramento Ambiental , Hospitalização/estatística & dados numéricos , Saúde da População Urbana/estatística & dados numéricos , Poluentes Atmosféricos/efeitos adversos , Poluentes Atmosféricos/análise , Poluentes Atmosféricos/classificação , Poluição do Ar/análise , China/epidemiologia , Correlação de Dados , Depressão/epidemiologia , Depressão/terapia , Exposição Ambiental/efeitos adversos , Exposição Ambiental/prevenção & controle , Saúde Ambiental , Monitoramento Ambiental/métodos , Monitoramento Ambiental/estatística & dados numéricos , Feminino , Humanos , Revisão da Utilização de Seguros , Masculino
18.
PLoS One ; 15(4): e0231104, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32255794

RESUMO

Over the last three decades corals have declined precipitously in the Florida Keys. Their population decline has prompted restoration effort. Yet, little effort has been invested in understanding the contemporary niche spaces of coral species, which could assist in prioritizing conservation habitats. We sought to predict the probability of occurrence of 23 coral species, including the critically endangered Acropora cervicornis, using observations at 985 sites from 2011-2015. We ran boosted regression trees to evaluate the relationship between the presence of these corals and eight potential environmental predictors: (i) bathymetry (m), (ii) mean of daily sea surface temperature (SST) (°C), (iii) variance of SST (°C), (iv) range of SST (°C), (v) chlorophyll-a concentration (mg m3), (vi) turbidity (m-1), (vii) wave energy (kJ m-2), and (viii) distance from coast (km). The Marquesas and the lower and upper Florida Keys were predicted to support the most suitable habitats for the 23 coral species examined. A. cervicornis had one of the smallest areas of suitable habitat, which was limited to the lower and upper Florida Keys, the Dry Tortugas, and nearshore Broward-Miami reefs. The best environmental predictors of site occupancy of A. cervicornis were SST range (4-5°C) and turbidity (K490 between 0.15-0.25 m-1). Historically A. cervicornis was reported in clear oligotrophic waters, although the present results find the coral species surviving in nearshore turbid conditions. Nearshore, turbid reefs may shade corals during high-temperature events, and therefore nearshore reefs in south Florida may become important refuges for corals as the ocean temperatures continue to increase.


Assuntos
Distribuição Animal , Antozoários/fisiologia , Recifes de Corais , Espécies em Perigo de Extinção/estatística & dados numéricos , Recuperação e Remediação Ambiental , Animais , Clorofila A/análise , Espécies em Perigo de Extinção/tendências , Monitoramento Ambiental/estatística & dados numéricos , Florida , Temperatura Alta/efeitos adversos , Água do Mar/análise , Água do Mar/química
20.
PLoS One ; 15(3): e0229509, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32203555

RESUMO

Environmental data may be "large" due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates with nonlinear relationships, whereas spatial regression, when using reduced rank methods, has a reputation for good predictive performance when using many records that are spatially autocorrelated. In this study, we compare these two techniques using a data set containing the macroinvertebrate multimetric index (MMI) at 1859 stream sites with over 200 landscape covariates. A primary application is mapping MMI predictions and prediction errors at 1.1 million perennial stream reaches across the conterminous United States. For the spatial regression model, we develop a novel transformation procedure that estimates Box-Cox transformations to linearize covariate relationships and handles possibly zero-inflated covariates. We find that the spatial regression model with transformations, and a subsequent selection of significant covariates, has cross-validation performance comparable to random forests. We also find that prediction interval coverage is close to nominal for each method, but that spatial regression prediction intervals tend to be narrower and have less variability than quantile regression forest prediction intervals. A simulation study is used to generalize results and clarify advantages of each modeling approach.


Assuntos
Exposição Ambiental/efeitos adversos , Monitoramento Ambiental/métodos , Monitoramento Ambiental/estatística & dados numéricos , Modelos Estatísticos , Rios/química , Regressão Espacial , Humanos
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