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1.
Chemosphere ; 242: 125272, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31896182

RESUMO

Eutrophication pollution seriously threatens the sustainable development of Lake Taihu, China. In order to identify the primary parameters of water quality and the potential pollution sources, the water quality dataset of Lake Taihu (2010-2014) was analyzed with the water quality index (WQI) and multivariate statistical analysis methods. Principle component analysis/factor analysis (PCA/FA) and correlation analysis screened out five significant water quality indicators, i.e. potassium permanganate index (CODMn), total nitrogen (TN), total phosphorus (TP), chloride ion (Cl-) and dissolved oxygen (DO), to represent the whole datasets and evaluate the water quality with WQI. Since northwestern of Lake Taihu was the most heavily polluted area, the parameters of the water quality were analyzed to further explore the potential sources and their contributions. Five potential pollution sources of northwestern lake were identified, and the contribution rate of each pollution source was calculated by the absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models. In brief, the PMF model was more suitable for pollution source apportionment of the northwestern lake, and the contribution rate was ranked as agricultural non-point source pollution (26.6%) > domestic sewage discharge (23.5%) > industrial wastewater discharge and atmospheric deposition (20.6%) > phytoplankton growth (16.0%) > rainfall or wind disturbance (13.4%). This study might provide useful information for the optimization of water quality management and pollution control strategies of Lake Taihu.


Assuntos
Monitoramento Ambiental/métodos , Lagos/química , Modelos Estatísticos , Poluentes Químicos da Água/análise , Qualidade da Água , China , Interpretação Estatística de Dados , Monitoramento Ambiental/estatística & dados numéricos , Eutrofização , Análise Fatorial , Modelos Lineares , Análise Multivariada , Análise de Componente Principal
2.
BJOG ; 127(3): 335-342, 2020 02.
Artigo em Inglês | MEDLINE | ID: mdl-31654606

RESUMO

OBJECTIVE: Asian dust is a natural phenomenon in which dust particles are transported from desert areas in China and Mongolia to East Asia. Short-term exposure to Asian dust has been associated with cardiovascular disease through mechanisms such as systemic inflammation. Because inflammation is a potential trigger of placental abruption, exposure may also lead to abruption. We examined whether exposure to Asian dust was associated with abruption. DESIGN: A bi-directional, time-stratified case-crossover design. SETTING AND POPULATION: From the Japan Perinatal Registry Network database, we identified 3014 patients who delivered singleton births in hospitals in nine Japanese prefectures from 2009 to 2014 with a diagnosis of placental abruption. METHODS: Asian dust levels were measured at Light Detection and Ranging monitoring stations, and these measurements were used to define the Asian dust days. As there was no information on the onset day of abruption, we assumed this day was the day before delivery (lag1). MAIN OUTCOME MEASURES: Placental abruption. RESULTS: During the study period, the Asian dust days ranged from 15 to 71 days, depending on the prefecture. The adjusted odds ratio of placental abruption associated with exposure to Asian dust was 1.4 (95% confidence interval = 1.0, 2.0) for cumulative lags of 1-2 days. Even after adjustment for co-pollutant exposures, this association did not change substantially. CONCLUSIONS: In this Japanese multi-area study, exposure to Asian dust was associated with an increased risk of placental abruption. TWEETABLE ABSTRACT: Exposure to environmental factors such as Asian dust may be a trigger of placental abruption.


Assuntos
Descolamento Prematuro da Placenta , Poeira , Monitoramento Ambiental , Exposição por Inalação/efeitos adversos , Descolamento Prematuro da Placenta/diagnóstico , Descolamento Prematuro da Placenta/epidemiologia , Adulto , Estudos Cross-Over , Monitoramento Ambiental/métodos , Monitoramento Ambiental/estatística & dados numéricos , Feminino , Humanos , Sistemas de Informação/estatística & dados numéricos , Japão/epidemiologia , Gravidez , Medição de Risco , Fatores de Risco
3.
Environ Monit Assess ; 191(11): 675, 2019 Oct 25.
Artigo em Inglês | MEDLINE | ID: mdl-31654143

RESUMO

Subtropical scrub forests in Pakistan have diminished by about 75% over the last hundred years, mainly due to indiscriminate exploitation and invasion by exotics species. Lack of initiatives, awareness, and research in utilizing the techniques used for accelerating natural forest succession is resulting in further degradation of the remaining forests. To promote active restoration with local communities and governmental authorities, a restoration scheme was piloted between 2010 and 2016 to examine enrichment population effects. Over 4,000 saplings of two woody climax species, Acacia modesta and Olea ferruginea, raised from seeds of local provenance, were planted in three subjectively selected trial plots representing various stages of degradation, covering a total area of about 4 ha. The results showed an overall 46% survival rate, accompanied by natural regeneration. Comparative analyses of the trial plots have shown variations which were strongly site specific, in addition, it also helped in gauging compliance of the site coordinators in implementing restoration measures as an effective management tool. This study provided an opportunity to appreciate the differences in terms of interventions used for implementing ecological restoration across landscape in the degraded scrub forests.


Assuntos
Acacia/crescimento & desenvolvimento , Conservação dos Recursos Naturais/métodos , Monitoramento Ambiental/estatística & dados numéricos , Florestas , Olea/crescimento & desenvolvimento , Ecologia , Paquistão , Plantas , Sementes , Árvores
4.
Environ Monit Assess ; 191(11): 640, 2019 Oct 05.
Artigo em Inglês | MEDLINE | ID: mdl-31586228

RESUMO

The purpose of this study was to use water quality indices and statistical methods based on physicochemical parameters for spatial-temporal assessment of Ekbatan lake pollution. Sampling stations were selected based on systematic, non-random approach, and it was performed in 7 stations, with intervals of 30 days in 2010-2011. At first, indices model of IRWQI, NSFWQI, and TLI was prepared in GIS environment based on qualitative parameters by interpolation functions (deterministic and geostatistical methods). Results demonstrated that qualitative parameters of FC, NO3-, BOD5, DOSat%, pH, PO43- and Turb respectively with annual average quality of 13.5 very bad, 27.4 bad, 46.8, 47.2 and 48.6 moderate, 70 goodish, and 71 good using statistical and geostatistical methods-accounted for highest pollution level and created greatest impact on lake pollution status. This amount was considered as bad and eutrophication quality on the southern and east margins of the lake because of the effluent load of the rivers under high land use activities (especially residential and then agricultural) in comparison with the lake core and north (showing baddish and moderate quality status) has more pollution level than that. Also, all parameters showed a high positive correlation and significance with increased of water temperature owing to increase in pollutant land use activities and condensation of pollutants and decreased of floodrains in warm seasons, and two parameters of TDS and Turb show high significance rather than beginning floodrains in spring and autumn. Also, lake was in qualitative status of baddish 43.7, moderate 58.2, and mild eutrophication 57.2 based on IRWQI, NSFWQI, and TLIChl.a respectively. Further, according to IRWQI index, the quality of lake in summer was in baddish status of 35, in half of initial winter in goodish condition of 56 and spring and autumn in moderate qualitative status of 46.


Assuntos
Monitoramento Ambiental/métodos , Monitoramento Ambiental/estatística & dados numéricos , Lagos/química , Modelos Teóricos , Poluição da Água/análise , Qualidade da Água , Agricultura , Eutrofização , Irã (Geográfico) , Rios/química , Estações do Ano , Análise Espaço-Temporal , Temperatura Ambiente
5.
Environ Pollut ; 255(Pt 1): 113191, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31542668

RESUMO

This paper reports the results of a joint project carried out by three regional environmental agencies of Italy to evaluate long-range mercury (Hg) transport from the abandoned Mt. Amiata Hg district in southern Tuscany (the third largest worldwide site for Hg production) to the fluvial ecosystems of the Paglia and Tiber rivers. Most of the work focused on stream sediments, surface waters and soils. A preliminary survey of Hg0 content in air was also conducted. Data obtained by public health authorities on Hg in vegetables and fish were also included. The highest Hg concentrations (up to thousands of µg/g Hg) were observed in stream sediments and soils directly impacted by Hg mine runoff. Although progressive Hg dilution was observed from north to south along the river, sediments and soils show anomalous Hg levels for over 200 km downstream of Mt. Amiata, testifying to an extreme case of long-range Hg contamination. A pervasive redistribution of Hg is observed in all sediment compartments. Presumably, the width of the impacted fluvial corridor corresponds to the entire alluvial plains of the rivers. The floodplains can be considered new sources for downstream Hg redistribution, especially during large flood events. On the other hand, results from water, air, and vegetable sampling indicate low potential for human exposure to Hg. The extent and distribution of the contamination make remediation not viable. Therefore, people and human activities must coexist with such an anomaly. On the technical side, the most urgent action to be taken is a better definition of the exact extent of the contaminated area. On the management side, it is necessary to identify which public institution(s) can best deal with such a widespread phenomenon. According to the precautionary principle, the impact of the contamination on human activities in the affected areas should be considered.


Assuntos
Monitoramento Ambiental/estatística & dados numéricos , Sedimentos Geológicos/química , Mercúrio/análise , Rios/química , Solo/química , Poluentes Químicos da Água/análise , Animais , Conservação dos Recursos Naturais , Ecossistema , Exposição Ambiental/estatística & dados numéricos , Peixes , Humanos , Itália
6.
Artigo em Inglês | MEDLINE | ID: mdl-31500255

RESUMO

Quantifying the air pollution and health impacts of transportation plans provides decision makers with valuable information that can help to target interventions. However, a large number of environmental epidemiological research assumes exposures of static populations at residential locations and does not consider the human activity patterns, which may lead to significant estimation errors. This study uses an integrated modeling framework to predict fine-grained air pollution exposures occurring throughout residents' activity spaces. We evaluate concentrations of fine particulate matter (PM2.5) under a regional transportation plan for Sacramento, California, using activity-based travel demand model outputs, vehicle emission, and air dispersion models. We use predicted air pollution exposures at the traffic analysis zone (TAZ) level to estimate residents' exposure accounting for their movements throughout the day to assess the impact of activity-based mobility pattern on air pollution exposure. Results of PM2.5 exposures estimated statically (at residential locations) versus dynamically (over residents' activity-based mobility) demonstrates that the two methods yield statistically significant different results (p < 0.05). In addition, the comparison conducted in different age groups shows that the difference between these two approaches is greater among youth and working age residents, whereas seniors show a similar pattern using both approaches due to their lower rates of travel activity.


Assuntos
Poluentes Atmosféricos/análise , Poluição do Ar/análise , Exposição Ambiental/análise , Monitoramento Ambiental/métodos , Material Particulado/análise , Emissões de Veículos/análise , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Poluição do Ar/estatística & dados numéricos , California , Exposição Ambiental/estatística & dados numéricos , Monitoramento Ambiental/estatística & dados numéricos , Feminino , Humanos , Masculino , Pessoa de Meia-Idade
7.
Ecotoxicol Environ Saf ; 183: 109510, 2019 Nov 15.
Artigo em Inglês | MEDLINE | ID: mdl-31401332

RESUMO

Water quality assessment and monitoring is one of the most important aspects for ensuring a good environmental health. A Water Quality Index (WQI) is one of the most frequently used tools for assessing overall quality of water resources. This study uses Factor Analysis (FA) for one of the most significant steps in WQI development - weight determination. Factor analysis has been applied to the water quality parameters shortlisted from Principal Component Analysis in the study area and it grouped the parameters into different sets of loadings. Each loading contained a group of parameters contributing to the overall variance addressed by that loading. Weights were allocated to each loading as well as to individual parameters within each loading. For final aggregation, a hybrid method was followed; where weighted harmonic means were estimated for the parameters within each loading and weighted arithmetic mean was estimated from the results of harmonic mean. The use of multivariate statistical technique reduces the subjectivity in the development of the final WQI and makes the current study a useful step in future for the development of a Ganga Water Quality Index (GWQI). In addition to this, the developed methodology can also be used for developing WQI for any water body depending on the availability of historical data.


Assuntos
Monitoramento Ambiental/métodos , Monitoramento Ambiental/estatística & dados numéricos , Qualidade da Água/normas , Recursos Hídricos , Análise Fatorial , Análise de Componente Principal , Alocação de Recursos
8.
Environ Monit Assess ; 191(9): 568, 2019 Aug 15.
Artigo em Inglês | MEDLINE | ID: mdl-31418094

RESUMO

Accurate estimates of total global solar irradiance reaching the Earth's surface are relevant since routine measurements are not always available. This work aimed to determine which of the models used to estimate daily total global solar irradiance (TGSI) is the best model when irradiance measurements are scarce in a given site. A model based on an artificial neural network (ANN) and empirical models based on temperature and sunshine measurements were analyzed and evaluated in Córdoba, Argentina. The performance of the models was benchmarked using different statistical estimators such as the mean bias error (MBE), the mean absolute bias error (MABE), the correlation coefficient (r), the Nash-Sutcliffe equation (NSE), and the statistics t test (t value). The results showed that when enough measurements were available, both the ANN and the empirical models accurately predicted TGSI (with MBE and MABE ≤ |0.11| and ≤ |1.98| kWh m-2 day-1, respectively; NSE ≥ 0.83; r ≥ 0.95; and |t values| < t critical value). However, when few TGSI measurements were available (2, 3, 5, 7, or 10 days per month) only the ANN-based method was accurate (|t value| < t critical value), yielding precise results although only 2 measurements per month were available for 1 year. This model has an important advantage over the empirical models and is very relevant to Argentina due to the scarcity of TGSI measurements.


Assuntos
Monitoramento Ambiental/métodos , Modelos Teóricos , Luz Solar , Argentina , Monitoramento Ambiental/estatística & dados numéricos , Análise de Regressão , Temperatura Ambiente
9.
Mar Pollut Bull ; 146: 962-976, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31426244

RESUMO

Past major oil spill disasters, such as the Prestige or the Deepwater Horizon accidents, have shown that spilled oil may drift across the ocean for months before being controlled or reaching the coast. However, existing oil spill modelling systems can only provide short-term trajectory simulations, being limited by the typical met-ocean forecast time coverage. In this paper, we propose a methodology for mid-long term (1-6 months) probabilistic predictions of oil spill trajectories, based on a combination of data mining techniques, statistical pattern modelling and probabilistic Lagrangian simulations. Its main features are logistic regression modelling of wind and current patterns and a probabilistic trajectory map simulation. The proposed technique is applied to simulate the trajectory of drifting buoys deployed during the Prestige accident in the Bay of Biscay. The benefits of the proposed methodology with respect to existing oil spill statistical simulation techniques are analysed.


Assuntos
Monitoramento Ambiental/métodos , Previsões/métodos , Poluição por Petróleo/análise , Poluentes Químicos da Água/análise , Simulação por Computador , Monitoramento Ambiental/estatística & dados numéricos , Modelos Logísticos , Oceanos e Mares , Poluição por Petróleo/estatística & dados numéricos , Movimentos da Água , Vento
10.
Environ Monit Assess ; 191(9): 532, 2019 Aug 02.
Artigo em Inglês | MEDLINE | ID: mdl-31375933

RESUMO

Macajalar Bay in the southern Philippines has become an attractive thoroughfare with recent developments, rendering anthropogenic input to the coastal waters. Expediting coastal resource management strategies necessitates the present study on coastal water characteristics. This was aided with distribution pattern and multivariate analyses for apportioning possible anthropogenic inputs. A total of 15 biophysicochemical characteristics were studied covering two municipalities (Opol and Jasaan) with six subcoastal communities in 2017. Data were all processed for Q test to eliminate outliers before distribution analyses using univariate (descriptive), inferential (t test, one-way ANOVA, Pearson correlation), and multivariate statistics (hierarchal cluster analysis (HCA) and principal component analysis (PCA)). Overall, higher concentrations were determined in the ecotourism site (Opol) than in the industrial site (Jasaan) as sampling months progressed except for oil and grease. Results for total coliform, fecal coliform, heterotrophic plate count (HPC), total suspended solids (TSS), chemical oxygen demand (COD), and oil and grease regardless of spatial-temporal variations exceeded the standards. Distribution pattern revealed variations selectively for pH, temperature, dissolved oxygen (DO), and oil and grease, indicating site-specific distribution. HCA and PCA results corroborated correlation matrices showing elevated concentrations in an ecotourism site (Opol) apportioned anthropogenic input mainly due to rural development and ecotourism. Likewise, in the industrial site (Jasaan), HCA and PCA results reflected possible anthropogenic input from rural development and industries. Overall, anthropogenic apportionment in the bay was influenced by rural development, ecotourism, and industries.


Assuntos
Baías/química , Monitoramento Ambiental/métodos , Atividades Humanas , Poluição da Água/análise , Análise da Demanda Biológica de Oxigênio , Análise por Conglomerados , Monitoramento Ambiental/estatística & dados numéricos , Análise Multivariada , Filipinas , Análise de Componente Principal , Temperatura Ambiente , Urbanização
11.
Environ Monit Assess ; 191(8): 524, 2019 Jul 30.
Artigo em Inglês | MEDLINE | ID: mdl-31363924

RESUMO

Some environmental studies use non-probabilistic sampling designs to draw samples from spatially distributed populations. Unfortunately, these samples can be difficult to analyse statistically and can give biased estimates of population characteristics. Spatially balanced sampling designs are probabilistic designs that spread the sampling effort evenly over the resource. These designs are particularly useful for environmental sampling because they produce good-sample coverage over the resource, they have precise design-based estimators and they can potentially reduce the sampling cost. The most popular spatially balanced design is Generalized Random Tessellation Stratified (GRTS), which has many desirable features including a spatially balanced sample, design-based estimators and the ability to select spatially balanced oversamples. This article considers the popularity of spatially balanced sampling, reviews several spatially balanced sampling designs and shows how these designs can be implemented in the statistical programming language R. We hope to increase the visibility of spatially balanced sampling and encourage environmental scientists to use these designs.


Assuntos
Monitoramento Ambiental/estatística & dados numéricos , Modelos Estatísticos , Biometria , Monitoramento Ambiental/métodos , Humanos , Distribuição Aleatória , Projetos de Pesquisa , Amostragem , Inquéritos e Questionários
12.
Environ Monit Assess ; 191(8): 509, 2019 Jul 24.
Artigo em Inglês | MEDLINE | ID: mdl-31342188

RESUMO

The aim of this paper is to provide a methodology including statistical tools and spatial techniques, in order to identify the various potential sources of chromium (Crtot) in the Sarigkiol basin, Western Macedonia, Greece, where elevated concentrations of Crtot in groundwater have been recorded since 1996. Integrated hydrochemical approach and statistical analyses including Pearson's correlation coefficient, multivariate statistical analyses (factor analysis and hierarchical cluster analysis), and spatial techniques (Moran's I spatial autocorrelation index and bivariate local indicator spatial association cluster map) were applied to evaluate the chemical analyses of 73 water samples, from irrigation wells, natural springs, and surface water. Both natural and anthropogenic sources of Crtot were recorded; the first (ultramafic-dominated environment) is strongly depicted on the natural spring water, in which Crtot concentrations as high as ~ 130 µg/L were recorded, whereas the second (agricultural activities) acts synergistically in the irrigation wells of the Sarigkiol basin, in which strong correlations of Crtot, P, and NO3- were defined. The paper highlights its findings by outlining the potential sources of elevated concentrations of Cr6+ in the Sarigkiol basin, stressing the need for a closer attention on the role of agricultural activities as an important, though commonly neglected, anthropogenic source of Crtot in groundwater.


Assuntos
Cromo/análise , Monitoramento Ambiental/métodos , Água Subterrânea/química , Rios/química , Poluentes Químicos da Água/análise , Poços de Água , Agricultura , Monitoramento Ambiental/estatística & dados numéricos , Grécia , Análise Multivariada , Análise Espacial
13.
Environ Sci Pollut Res Int ; 26(25): 25676-25689, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31267397

RESUMO

The objective of the study is to conduct the socio-economic and environmental survey about the feasibility of Saudi Arabia-China-Pakistan Economic Corridor (SCPEC) in five different dimensions, i.e., (i) key strengths of SCPEC project, (ii) prospective weaknesses of SCPEC project, (iii) opportunities attain Pakistan from SCPEC project, (iv) opportunities gain SCPEC from Pakistan, and (v) possible threats from SCPEC to other countries, including India. The larger number of intellects participated in this survey, including armed personnel working in strategic industries, academicians of higher education institutes, colleges, and public/private schools, doctors, civil servants, employees of non-governmental organizations, and others. The survey identified five major key strengths, including tourism promotion, infrastructure development, technology diffusion, energy demand, and mutual trade gains, while the prospective weaknesses are financial constraints, political instability, international dumping, corruption, and lack of good governance. The survey results show that Pakistan economy could attain maximum opportunities from SCPEC project in the form of economic empowerment, mutual trade gains, transportation development, entrepreneurship, and development of Gwadar port, while the SCPEC project gains from Pakistan in the form of economic stabilization, trade gains, and low transportation cost. The possible threats to SCPEC project to the other countries including India are political threats, security issues, Kashmir issue, and economic issues. The survey results conclude that the large number of intellects confirmed the positivity of SCPEC project for both the Pakistan and for the Chinese economy, while few intellects in numbers are incompatible with the SCPEC project due to economic, environmental, and security threats.


Assuntos
Monitoramento Ambiental , China , Economia , Monitoramento Ambiental/estatística & dados numéricos , Governo , Humanos , Índia , Paquistão , Estudos Prospectivos , Arábia Saudita
14.
Ecotoxicol Environ Saf ; 182: 109386, 2019 Oct 30.
Artigo em Inglês | MEDLINE | ID: mdl-31255868

RESUMO

It is highly significant to develop efficient soft sensors to estimate the concentration of hazardous pollutants in a region to maintain environmental safety. In this paper, an air quality warning system based on a robust PM2.5 soft sensor and support vector machine (SVM) classifier is reported. The soft sensor for the estimation of PM2.5 concentration is proposed using a novel approach of Bayesian regularized neural network (BRNN) via forward feature selection (FFS). Zuoying district of Taiwan is selected as the region of study for implementation of the estimation system because of the high pollution in the region. Descriptive statistics of various pollutants in Zuoying district is computed as part of the study. Moreover, seasonal variation of particulate matter (PM) concentration is analyzed to evaluate the impact of various seasons on the increased levels of PM in the region. To investigate the linear dependence of concentration of different pollutants to the concentration of PM2.5, Pearson correlation coefficient, Kendall's tau coefficient, and Spearman coefficient are computed. To achieve high performance for the PM2.5 estimation, selection of appropriate forward features from the input variables is carried out using FFS technique and Bayesian regularization is incorporated to the neural network system to avoid the overfitting problem. The comparative evaluation of performance of BRNN/FFS estimation system with various other methods shows that our proposed estimation system has the lowest mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). Moreover, the coefficient of determination (R-squared) is around 0.95 for the proposed estimation method, which denotes a good fit. Evaluation of the SVM classifier showed good performance indicating that the proposed air quality warning system is efficient.


Assuntos
Poluentes Atmosféricos/análise , Poluição do Ar/análise , Monitoramento Ambiental/métodos , Material Particulado/análise , Teorema de Bayes , Monitoramento Ambiental/estatística & dados numéricos , Estações do Ano , Taiwan
15.
Environ Monit Assess ; 191(7): 473, 2019 Jun 29.
Artigo em Inglês | MEDLINE | ID: mdl-31256248

RESUMO

Dengue is among the largest public health problems in Brazil. Reported dengue cases via DATASUS were correlated with reanalysis data from NCEP (rainfall and air temperature) and Brazil's population data (2000 and 2010) from 1994 to 2014. The aim of this study was to evaluate relational patterns between climate variables together with population data from the last census and reported cases of dengue in Brazil from 1994 to 2014 by using statistical techniques. Several statistical methods [descriptive and exploratory statistics; simple and multiple linear regressions; Mann-Kendall (MK), Run, and Pettit nonparametric tests; and multivariate statistics via cluster analysis (CA)] were applied to time series. The highest percentages of Dengue cases were in Brazil's Southeast (47.14%), Northeast (29.86%), and Central West (13.01%). Upon CA of the Brazilian regions, three homogeneous dengue groups were formed: G1 (North and Central West), G2 (Southeast and Northeast), and G3 (South). Run testing indicated that the time series is homogenous and persistence free. MK testing showed a nonsignificant trend of increase of dengue cases in 23 states with positive trends and in four states with negative trends of Brazil. A significant increase in the magnitude of dengue at the regional level was recorded in the North, Southeast, South, and Central West regions. Statistical methods showed that dengue variability in Brazil is cyclical (2- to 3-year cycles), but not repetitive of El Niño-Southern Oscillation (ENSO) in the moderate, strong, and neutral categories. ENSO interferes with the action of weather systems, changing or intensifying rainfall and air temperatures in Brazil. The population increase in recent decades and the lack of effective public policies together with the action of ENSO contributed to the increase in dengue cases reported in Brazil.


Assuntos
Dengue/epidemiologia , Monitoramento Ambiental/estatística & dados numéricos , Brasil/epidemiologia , Mudança Climática , El Niño Oscilação Sul , Monitoramento Epidemiológico , Humanos , Análise Multivariada , Estatísticas não Paramétricas , Tempo (Meteorologia)
16.
Environ Monit Assess ; 191(Suppl 2): 366, 2019 Jun 28.
Artigo em Inglês | MEDLINE | ID: mdl-31254075

RESUMO

The spatial distribution of the prevalence of asthma and chronic obstructive pulmonary disease (COPD) remains under the influence of a wide array of environmental, climatic, and socioeconomic determinants. However, a large proportion of these influences remain unexplained. In completion, this study examined the spatial associations between asthma/COPD morbidity and their determinants using ordinary least squares (OLS) and geographically weighted regressions (GWR). Inpatient records collected from the secondary and tertiary care hospitals in Kandy from 2010 to 2014 were considered as the dependent variable. Potential risk factors (explanatory variables) were identified in four distinguished classes: 1) meteorological factors, (2) direct and indirect factors of air pollution, (3) socioeconomic factors, and (4) characteristics of the physical environment. All possible combinations of candidate explanatory variables were evaluated through an exploratory regression. A comparison between the regression models was also explored. The best OLS regression models revealed about 55% of asthma variation and 62% of COPD variation while GWR models yielded 78% and 74% of the variation of asthma and COPD occurrences respectively. Relative humidity, proximity to roads (0-200 m), road density, use of firewood as a source of fuel, and elevation play a vital role in predicting morbidity from asthma and COPD. Both local and global regression models are important in assessing spatial relationships of asthma and COPD. However, the local models exhibit a better prediction capability for assessing non-stationary relationships of asthma and COPD than global models. The geostatistical aspects used in this study may also provide insights for evaluating heterogeneous environmental risk factors in other epidemiological studies across different spatial settings.


Assuntos
Asma/epidemiologia , Geografia Médica/métodos , Modelos Estatísticos , Doença Pulmonar Obstrutiva Crônica/epidemiologia , Monitoramento Ambiental/estatística & dados numéricos , Humanos , Análise dos Mínimos Quadrados , Prevalência , Fatores de Risco , Fatores Socioeconômicos , Regressão Espacial , Sri Lanka/epidemiologia
17.
Environ Monit Assess ; 191(Suppl 2): 328, 2019 Jun 28.
Artigo em Inglês | MEDLINE | ID: mdl-31254078

RESUMO

In this study, Moderate Resolution Imaging Spectrometer (MODIS) satellite measurements of aerosol optical depth (AOD) from different retrieval algorithms have been correlated with ground measurements of fine particulate matter less than 2.5 µm (PM2.5). Several MODIS AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target vs. Deep Blue), collections (5.1 vs. 6), and spatial resolutions (10 km vs. 3 km) for cities in the Western, Midwestern, and Southeastern USA have been evaluated. We developed and validated PM2.5 prediction models using remotely sensed AOD data. These models were further improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind gust, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the simulation quality of all the PM2.5 models, especially in the Western USA. Temperature, relative humidity, and wind gust were significant meteorological variables throughout the year in the Western USA. Wind speed was the most significant meteorological variable for the cold season while for the warm season, temperature was the most prominent one in the Midwestern and Southeastern USA. Using this satellite-derived PM2.5 data can improve the spatial coverage, especially in areas where PM2.5 ground monitors are lacking, and studying the connections between PM2.5 and public health concerns including respiratory and cardiovascular diseases in the USA can be further advanced.


Assuntos
Aerossóis/análise , Poluentes Atmosféricos/análise , Monitoramento Ambiental/estatística & dados numéricos , Material Particulado/análise , Saúde Pública/métodos , Monitoramento Ambiental/métodos , Tamanho da Partícula , Tecnologia de Sensoriamento Remoto/estatística & dados numéricos , Reprodutibilidade dos Testes , Estações do Ano , Tempo (Meteorologia)
18.
Environ Monit Assess ; 191(Suppl 2): 326, 2019 Jun 28.
Artigo em Inglês | MEDLINE | ID: mdl-31254083

RESUMO

Dependency on groundwater has increased due to unprecedented growth of industries as well as settlements. Therefore, assessment of groundwater quality to determine its impact on human and environment has become essential. The major objective of this study was to frame a methodology for complete assessment of groundwater quality in a highly industrialized area comprising of iron, steel, fertilizer, cement, chemical, heavy machinery manufacturing, thermal power, coal mining, and allied industries. Physico-chemical parameters of water samples were analyzed from strategic locations during pre- and post-monsoon seasons. The primary analysis through the water quality index showed 50% of the sampling locations in pre-monsoon and 65% in post-monsoon seasons have very poor quality. Hence, the health risk calculated through hazard index indicates that the water is unsafe for drinking. Chemical indices such as sodium percentage, sodium adsorption ratio, residual sodium carbonate, permeability index, and magnesium hazard suggest that the water can be used for irrigation. High corrosivity ratio at 90% sampling locations specifies its unsuitability for use in industrial production. Factor analysis and other statistical methods justified that the pollution of groundwater was attributed to geogenic, as well as anthropogenic, activities. This research demonstrates the usefulness of interdisciplinary techniques for complete assessment of groundwater quality and representation of complex data set into a presentable and understandable form for proper communication with public, regulatory authorities, as well as policy makers, responsible for water management.


Assuntos
Monitoramento Ambiental/estatística & dados numéricos , Água Subterrânea/análise , Indústrias/estatística & dados numéricos , Monitoramento Ambiental/métodos , Análise Fatorial , Água Subterrânea/química , Água Subterrânea/normas , Humanos , Índia , Indústrias/classificação , Medição de Risco , Estações do Ano , Poluentes Químicos da Água/análise , Qualidade da Água
19.
Environ Monit Assess ; 191(Suppl 2): 337, 2019 Jun 28.
Artigo em Inglês | MEDLINE | ID: mdl-31254087

RESUMO

For the period of the Barnett Coordinated Campaign, October 16-31, 2013, hourly concentrations for 46 volatile organic compounds (VOCs) were recorded at 14 air monitoring stations within the Barnett Shale of North Texas. These measurements are used to identify and analyze multi-species hydrocarbon signatures on a regional scale through the novel combination of two techniques: domain filling with Lagrangian trajectories and the machine learning unsupervised classification algorithm called a self-organizing map (SOM). This combination of techniques is shown to accurately identify concentration enhancements in the lightest measured alkane species at and downwind of the locations of active-permit oil and gas facilities, despite the model having no a priori knowledge of these source locations. Site comparisons further identify the SOM's ability to distinguish between signatures with differing influences from oil- and gas-related processes and from urban processes. A random forest (a machine learning supervised classification) analysis is conducted to further probe the sensitivities of the SOM classification in response to changes in any hydrocarbon species' concentration values. The random forest analysis of four representative classes finds that the SOM classification is appropriately more sensitive to changes in certain urban-related species for urban-related classes, and to changes in oil- and gas-related species for oil- and gas-related classes.


Assuntos
Poluentes Atmosféricos/análise , Monitoramento Ambiental/métodos , Hidrocarbonetos/análise , Gás Natural/análise , Compostos Orgânicos Voláteis/análise , Algoritmos , Cidades , Monitoramento Ambiental/estatística & dados numéricos , Aprendizado de Máquina , Campos de Petróleo e Gás , Texas
20.
Environ Monit Assess ; 191(Suppl 2): 323, 2019 Jun 28.
Artigo em Inglês | MEDLINE | ID: mdl-31254088

RESUMO

This paper highlights the advantages of multivariate statistical and geostatistical methods to compile the hydro-geochemical properties of groundwater. A total of 123 samples were collected from wells located in Saveh aquifer, in 2015. Seven parameters including total dissolved solids (TDS), sodium adsorption ratio( SAR), electrical conductivity (EC), sodium (Na+), total hardness (TH), chloride (Cl-), and sulfate (SO42-) were analyzed, compiled, and interpreted statistically and geostatistically. At first, factor analysis gave rise to produce a factor representing 94% of the variability. Also, variography was calculated and compiled to define spatial regression and experimental variograms were plotted by GS+ software, then, the best theoretical models were fitted on the variograms and an estimation map was prepared based on geostatistical relationship presented in the paper. Smoothing effect is one of the main drawbacks of forward geostatistical methods, on the contrary, inversed methods are subjected to no smoothing effect. Results showed that geostatistical inversed methods could reveal more reliable results than forward methods. Eventually, the map of the estimated factor, as well as error maps, was compiled. According to the evaluation of fractal dimensions, the estimated factor explained the variability of all hydrogeochemical parameters and groundwater quality was categorized as the safe, normal, and anomalous class, ranged from - 1.10 to 1.10, 1.11 to 3.1, and more than 3.1, respectively.


Assuntos
Monitoramento Ambiental/métodos , Água Subterrânea/química , Condutividade Elétrica , Monitoramento Ambiental/estatística & dados numéricos , Análise Fatorial , Água Subterrânea/normas , Íons/análise , Modelos Teóricos , Poluentes Químicos da Água/análise , Qualidade da Água , Poços de Água
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