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1.
Int J Biometeorol ; 64(4): 601-610, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-31942644

RESUMO

Cases of anthrax in livestock are infrequently and irregularly reported in the state of Victoria, Australia; however, their impact on individual livestock, farming communities and the government agencies tasked with containing these outbreaks is high. This infrequency has been anecdotally associated with differences in annual and local weather patterns. In this study, we used historical anthrax cases and meteorological data from weather stations throughout Victoria to train a generalized linear mixed effects model to predict the daily odds of a case of anthrax occurring in each shire in the coming 30 days. Meteorological variables were transformed to deviations from the mean values for temperature or cumulative values for rainfall in the shire across all years. Shire was incorporated as a random effect to account for meteorological variation between shires. The model incorporated a post hoc weighting for the frequency of historic cases within each shire and the spatial contribution of each shire to the recently redefined Australian Anthrax Belt. Our model reveals that anthrax cases were associated with drier summer conditions (OR 0.96 (95% CI 0.95-0.97) and OR 0.98 (95% CI 0.97-0.99) for every mm increase in rainfall during September and December, respectively) and cooler than average spring (OR 0.20 (95% CI 0.11-0.52) for every °C increase in minimum daily temperature during November and warmer than average summer temperatures (OR 1.45 (95% CI 1.29-1.61) for every °C increase in maximum daily temperature during January. Cases were also preceded by a 40-day period of cooler, drier temperatures (OR 0.5 (95% CI 0.27-0.74) for every °C increase in maximum daily temperature and OR 0.96 (95% CI 0.95-0.97) for every mm increase in rainfall followed by a warmer than average minimum (or nightly) temperature 10 days immediately before the case (OR 1.46 (95% CI 1.35-1.58) for every °C increase in maximum daily temperature). These coefficients of this training model were then applied daily to meteorological data for each shire, and output of these models was presented as a choropleth and timeline plot in a Shiny web application. The application builds on previous spatial modelling and provides Victorian agencies with a tool to engage at-risk farmers and guide discussions towards anthrax control. This application can contribute to the wider rejuvenation of anthrax knowledge and control in Victoria and corroborates the anecdote that increased odds of disease can be linked to meteorological events.


Assuntos
Antraz , Meteorologia , Animais , Gado , Temperatura Ambiente , Vitória , Tempo (Meteorologia)
2.
Int J Biometeorol ; 64(1): 123-136, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31707494

RESUMO

Cold- and heat-related mortality poses significant public health concerns worldwide. Although there are numerous studies dealing with the association between extreme ambient temperature and mortality, only a small number adopt a synoptic climatological approach in order to understand the nature of weather systems that precipitate increases in cold- or heat-related mortality. In this paper, the Lamb Weather Type synoptic classification is used to examine the relationship between daily mortality and weather patterns across nine regions of England. Analysis results revealed that the population in England is more susceptible to cold weather. Furthermore, it was found that the Easterly weather types are the most hazardous for public health all-year-long; however, during the cold period, the results are more evident and spatially homogenous. Nevertheless, it is noteworthy that the most dangerous weather conditions are not always associated with extreme (high or low) temperatures, a finding which points to the complexity of weather-related health effects and highlights the importance of a synoptic climatological approach in elucidating the relationship between temperature and mortality.


Assuntos
Meteorologia , Tempo (Meteorologia) , Animais , Temperatura Baixa , Inglaterra , Temperatura Alta , Mortalidade , Estações do Ano , Ovinos
3.
Environ Pollut ; 256: 113395, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31708281

RESUMO

We apply convolutional neural network (CNN) model for estimating daily 24-h averaged ground-level PM2.5 of the conterminous United States in 2011 by incorporating aerosol optical depth (AOD) data, meteorological fields, and land-use data. Unlike some of the recent supervised learning-based approaches, which only utilized the predictors from the location of which PM2.5 value is estimated, we naturally aggregate predictors from nearby locations such that the spatial correlation among the predictors can be exploited. We carefully evaluate the performance of our method via overall, temporally-separated, and spatially-separated cross-validations (CV) and show that our CNN achieves competitive estimation accuracy compared to the recently developed baselines. Furthermore, we develop a novel predictor importance metric for our CNN based on the recent neural network interpretation method, Layerwise Relevance Propagation (LRP), and identify several informative predictors for PM2.5 estimation.


Assuntos
Poluentes Atmosféricos/análise , Monitoramento Ambiental , Material Particulado/análise , Aerossóis/análise , Meteorologia , Estados Unidos
4.
Chemosphere ; 241: 125026, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-31606570

RESUMO

With the principal aim to assess the typical Mediterranean profile of the PM2.5 and PM1 pollution, three intensive monitoring campaigns took place simultaneously within different types of environment across an urban location of the basin. Focusing on the PM components with numerous anthropogenic sources and increased potential health risk, the samples were chemically analyzed for 20 p.m.-bound Polycyclic Aromatic Hydrocarbons (PAHs). Carbonaceous and ionic constituents were quantified as well. In order to uncover the spatiotemporal variation of the PM profile the key sources were identified, the seasonal effects and the role of the prevailing mesoscale atmospheric circulation were evaluated and most importantly the potential health risk was estimated. In general, the pollution status of the basin was the result of a complex interaction between the local and external input with Particulate Organic Matter (POM) and Secondary Inorganic Aerosols (SIA) being the main aerosols' components. PM1 was a better indicator of the anthropogenic emissions while according to the results of factor analysis the co-existence of various combustion sources was determinant. Chemically, the maxima of the ΣPAHs, the differentiation of their structure in accordance with their molecular weight and the distribution of the individual compounds confirmed the significance of the emission sources. Similarly, the estimated carcinogenicity/mutagenicity was emission-dependent with the maximum contribution coming from B[a]P, IndP, B[ghi]Per, B[e]P and B[b]F. Seasonally, the highest potential health risk of the PAHs' mixture was recorded during the cold season while meteorologically, it was mostly associated with the south flow.


Assuntos
Poluentes Atmosféricos/análise , Monitoramento Ambiental/métodos , Meteorologia , Material Particulado/análise , Hidrocarbonetos Policíclicos Aromáticos , Medição de Risco , Aerossóis/análise , Poluição do Ar/análise , Grécia , Tamanho da Partícula , Hidrocarbonetos Policíclicos Aromáticos/análise , Estações do Ano
5.
Int J Biometeorol ; 64(3): 319-329, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31646388

RESUMO

The low availability of high-quality meteorological data resulted in the development of synthetic meteorological data generated by satellite or data interpolation, which are available in grids with varying spatio-temporal resolution. Among these different data sources, NASA/POWER and DailyGridded databases have been applied for crop yield simulations. The objective of this study was to evaluate the performance of these two datasets, in different time scales (daily, 10-day, monthly, and annual), as input data for estimating potential (YP) and attainable (YA) maize yields, using the FAO Agroecological Zone crop simulation model (FAO-AEZ), properly calibrated and validated. For that, daily weather data from ten Brazilian locations were collected and compared to the data extracted from NASA/POWER and DailyGridded systems and later applied to estimate the potential and attainable maize yields. DailyGridded data showed a better performance than NASA/POWER for all weather variables and time scales, with confidence index (C) ranging from 0.52 to 0.99 for the former and from 0.09 and 0.99 for the latter. As a consequence of that, DailyGridded data was better than NASA/POWER to estimate maize yields with estimates close to those obtained with observed data, with a lower mean absolute errors (< 30 kg ha-1) and a higher confidence index (C = 0.99).


Assuntos
Meteorologia , Zea mays , Brasil , Estados Unidos , United States National Aeronautics and Space Administration , Tempo (Meteorologia)
6.
Int J Biometeorol ; 64(3): 471-483, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31811392

RESUMO

Wearable devices have the potential to track and monitor a wide range of biometeorological conditions (e.g., temperature, UV, air quality) and health outcomes (e.g., mental stress, physical activity, physiologic strain, and cognitive impairments). These sensors provide the potential for personalized environmental exposure information that can be harnessed for at-risk populations. Personalized environmental exposure information is of particular importance for populations that are continuously exposed to hazardous environmental conditions or with underlying health conditions. Yet, for these devices to be effective, individuals must be willing to monitor their health and, if prompted, adhere to warnings or notifications. To date, no study has examined the perceptions and use of digital devices and wearable sensors among vulnerable outdoor working populations. This study evaluated digital device use and perceptions among a population of groundworkers in three diverse geographic sites in the southeastern USA (Boone, NC, Raleigh, NC, and Starkville, MS). Our results demonstrate that biometeorological health interventions should focus on smartphone technology as a platform for monitoring environmental exposure and associated health outcomes. It was encouraging to find that those study participants were very likely to wear sensors and utilize global positioning system technology despite potential privacy issues. In addition, 3 out of 4 workers indicated that they would change their behavior if given a personalized heat preventive warning. Future development of wearable sensors and smartphone applications should integrate personalized weather warnings and ensure privacy to facilitate the use of these technologies among vulnerable populations.


Assuntos
Meteorologia , Dispositivos Eletrônicos Vestíveis , Temperatura Alta , Humanos , Temperatura Ambiente , Tempo (Meteorologia)
7.
Int J Biometeorol ; 64(1): 157-163, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-29058080

RESUMO

Animal biometeorology (insects excluded) has been part of the International Journal of Biometeorology since its inception in 1958. Over the first 60 years of the journal, 480 animal biometeorology papers were published. Thus, approximately 14% of published papers dealt with animals. Over the first 60 years, data from more than 50 animal species was presented, with the lead authors coming from 48 countries. The two most common species used in animal papers between 1957 and 2016 were cattle (109 papers: 22.7% of all animal papers) and rats (96 papers: 20.0% of all animal papers). Although cattle and rats dominated, the species in the most cited paper (240 citations) was chickens, followed by bird migration (155 citations), and general livestock (118 citations). Overall, five papers exceeded 100 citations, and a further two exceeded 200 citations. In the last decade, 126 animal papers were published (26% of all animal papers). Many of these papers had a focus on livestock production in developing countries especially Brazil.


Assuntos
Meteorologia , Publicações Periódicas como Assunto , Publicações , Animais , Brasil , Bovinos , Galinhas , Gado , Ratos
8.
Int J Biometeorol ; 64(2): 205-216, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-29992355

RESUMO

Human comfort in outdoor spaces (HCOS) is linked to people's psychological responses to environmental variables. Previous studies have established comfort ranges for these variables through interviews and questionnaires, reaching only limited populations. However, larger amounts of data would not only generate more robust results in local studies, but it would also allow for the possibility of creating an approach that could be applied to a wider range of weather conditions and different climates. Therefore, this study describes a new methodology to assess people's perception of weather based on human responses to weather conditions extracted from tweets, with the purpose of establishing comfort ranges for environmental variables. Tweets containing weather-associated keywords were collected, stored, and then linked to real-time meteorological data acquired nearby the locations in which the tweets were posted. Afterwards, people's perception of weather was extracted from the tweets using a classifier trained specifically on weather data that identified irrelevant, neutral, positive, and negative tweets. The obtained tweets and their related atmospheric data were analyzed to establish comfort ranges. The tweets' responses to effective temperature were very similar to those obtained in previous studies, although the peak of comfort is shifted towards the cold stress. Similarly, the tweets' responses to the thermohygrometric index were alike to previous results, but the peak of comfort is shifted towards the heat stress. Regarding the single weather variables under study, the obtained comfort ranges are similar to the ones found in previous research; in particular, the temperature comfort range matches perfectly at 20-22 °C. Therefore, it was concluded that tweets can be used to assess HCOS; not only are the results of this methodology comparable to results obtained in previous studies, but the procedure itself also shows new features and unexpected future applications.


Assuntos
Meteorologia , Mídias Sociais , Clima , Humanos , Inquéritos e Questionários , Tempo (Meteorologia)
9.
Chemosphere ; 239: 124678, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31494323

RESUMO

In the developing countries such as China, most well-developed areas have suffered severe haze pollution, which was associated with increased premature morbidity and mortality and attracted widespread public concerns. Since ground-based PM2.5 monitoring has limited temporal and spatial coverage, satellite aerosol remote sensing data has been increasingly applied to map large-scale PM2.5 characteristics through advanced spatial statistical models. Although most existing research has taken advantage of the polar orbiting satellite instruments, a major limitation of the polar orbiting platform is its limited sampling frequency (e.g., 1-2 times/day), which is insufficient for capturing the PM2.5 variability during short but intense heavy haze episodes. As the first attempt, we quantitatively investigated the feasibility of using the aerosol optical depth (AOD) data retrieved by the Geostationary Ocean Color Imager (GOCI) to estimate hourly PM2.5 concentrations during winter haze episodes in the Yangtze River Delta (YRD). We developed a three-stage spatial statistical model, using GOCI AOD and fine mode fraction, as well as corresponding monitoring PM2.5 concentrations, meteorological and land use data on a 6-km modeling grid with complete coverage in time and space. The 10-fold cross-validation R2 was 0.72 with a regression slope of 1.01 between observed and predicted hourly PM2.5 concentrations. After gap filling, the R2 value for the three-stage model was 0.68. We further analyzed two representative large regional episodes, i.e., a "multi-process diffusion episode" during December 21-26, 2015 and a "Chinese New Year episode" during February 7-8, 2016. We concluded that AOD retrieved by geostationary satellites could serve as a new valuable data source for analyzing the heavy air pollution episodes.


Assuntos
Poluentes Atmosféricos/análise , Poluição do Ar/análise , Monitoramento Ambiental/métodos , Material Particulado/análise , Tecnologia de Sensoriamento Remoto/métodos , Aerossóis/análise , China , Meteorologia , Modelos Estatísticos , Rios , Estações do Ano , Astronave
10.
Neural Netw ; 121: 396-408, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31604202

RESUMO

In this study, we use a deep convolutional neural network (CNN) to develop a model that predicts ozone concentrations 24 h in advance. We have evaluated the model for 21 continuous ambient monitoring stations (CAMS) across Texas. The inputs for the CNN model consist of meteorology (e.g., wind field, temperature) and air pollution concentrations (NO x and ozone) from the previous day. The model is trained for predicting next-day, 24-hour ozone concentrations. We acquired meteorological and air pollution data from 2014 to 2017 from the Texas Commission on Environmental Quality (TCEQ). For 19 of the 21 stations in the study, results show that the yearly index of agreement (IOA) is above 0.85, confirming the acceptable accuracy of the CNN model. The results also show the model performed well, even for stations with varying monthly trends of ozone concentrations (specifically CAMS-012, located in El-Paso, and CAMS-013, located in Fort Worth, both with IOA=0.89). In addition, to ensure that the model was robust, we tested it on stations where fewer meteorological variables are monitored. Although these stations have fewer input features, their performance is similar to that of other stations. However, despite its success at capturing daily trends, the model mostly underpredicts the daily maximum ozone, which provides a direction for future study and improvement. As this model predicts ozone concentrations 24 h in advance with greater accuracy and computationally fewer resources, it can serve as an early warning system for individuals susceptible to ozone and those engaging in outdoor activities.


Assuntos
Poluentes Atmosféricos/análise , Aprendizado Profundo , Monitoramento Ambiental/métodos , Ozônio/análise , Poluição do Ar/análise , Previsões , Humanos , Meteorologia/métodos , Fatores de Tempo , Vento
11.
Sci Total Environ ; 698: 134246, 2020 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-31505344

RESUMO

The seasonal pollen index (SPI) is a continuing concern within the fields of aerobiology, ecology, botany, and epidemiology. The SPI of anemophilous trees, which varies substantially from year to year, reflects the flowering intensity. This intensity is regulated by two factors: weather conditions during flower formation and the inner resource for assimilation. A deterministic approach has to date been employed for predicting SPI, in which the forecast is made entirely by parameters. However, given the complexity of the masting mechanism (which has intrinsic stochastic properties), few attempts have been made to apply a stochastic model that considers the inter-annual SPI variation as a stochastic process. We propose a hidden Markov model that can integrate the stochastic process of mast flowering and the meteorological conditions influencing flower formation to predict the annual birch pollen concentration. In experiments conducted, the model was trained and validated by using data in Hokkaido, Japan covering 22 years. In the model, the hidden Markov sequence was assigned to represent the recurrence of mast years via a transition matrix, and the observation sequences were designated as meteorological conditions in the previous summer, which are governed by hidden states with emission distribution. The proposed model achieved accuracies of 83.3% in the training period and 75.0% in the test period. Thus, the proposed model can provide an alternative perspective toward the SPI forecast and probabilistic information of pollen levels as a useful reference for allergy stakeholders.


Assuntos
Poluição do Ar/estatística & dados numéricos , Monitoramento Ambiental/métodos , Pólen , Alérgenos , Betula , Fatores Biológicos , Previsões , Hipersensibilidade , Japão , Conceitos Meteorológicos , Meteorologia , Estações do Ano , Árvores , Tempo (Meteorologia)
12.
Environ Monit Assess ; 192(1): 25, 2019 Dec 10.
Artigo em Inglês | MEDLINE | ID: mdl-31823028

RESUMO

It is well documented that standalone machine learning methods are not suitable for rainfall forecasting in long lead-time horizons. The task is more difficult in arid and semiarid regions. Addressing these issues, the present paper introduces a hybrid machine learning model, namely multiple genetic programming (MGP), that improves the predictive accuracy of the standalone genetic programming (GP) technique when used for 1-month ahead rainfall forecasting. The new model uses a multi-step evolutionary search algorithm in which high-performance rain-borne genes from a multigene GP solution are recombined through a classic GP engine. The model is demonstrated using rainfall measurements from two meteorology stations in Lake Urmia Basin, Iran. The efficiency of the MGP was cross-validated against the benchmark models, namely standard GP and autoregressive state-space. The results indicated that the MGP statistically outperforms the benchmarks at both rain gauge stations. It may reduce the absolute and relative errors by approximately up to 15% and 40%, respectively. This significant improvement over standalone GP together with the explicit structure of the MGP model endorse its application for 1-month ahead rainfall forecasting in practice.


Assuntos
Previsões/métodos , Meteorologia/métodos , Modelos Genéticos , Chuva , Algoritmos , Irã (Geográfico)
13.
J Environ Manage ; 252: 109645, 2019 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-31610449

RESUMO

In the austral spring, biomass fires affect a vast area of South America each year. We combined in situ ozone (O3) data, measured in the states of São Paulo and Paraná, Brazil, in the period 2014-2017, with aerosol optical depth, co-pollutants (NOx, PM2.5 and PM10) and air backtrajectories to identify sources, transport and geographical patterns in the air pollution data. We applied cluster analysis to hourly O3 data and split the investigation area of approximately 290,000 km2 into five groups with similar features in terms of diurnal, weekly, monthly and seasonal O3 concentrations. All groups presented a peak in September and October, associated with the fire activities and enhanced photochemistry. The highest mean O3 concentrations were measured inland whilst, besides having lower concentrations, the coastal group was also associated with the smallest diurnal and seasonal variations. The latter was attributed to lower photochemical activity due to frequently occurring overcast weather situation. The mean annual regional contribution of O3 over the area was 61 µg/m3, with large seasonal and intersite variabilities (from 35 to 84 µg/m3). The long-range transport of smoke contributed with between 23 and 41% of the total O3 during the pollution events. A pollution outbreak in September 2015 caused many-fold increases in O3, PM2.5 and PM10 across the investigation area, which exceeded the World Health Organisation recommendations. We show that the regional transport of particulates and gas due to biomass burning overlays the local emissions in already highly polluted cities. Such an effect can outweigh local measures to curb anthropogenic air pollution in cities.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Meteorologia , Ozônio , Biomassa , Brasil , Cidades , Monitoramento Ambiental , Estações do Ano
14.
J Environ Manage ; 250: 109454, 2019 Nov 15.
Artigo em Inglês | MEDLINE | ID: mdl-31514001

RESUMO

In this paper, meteorological parameters, electric field strength and transmitters' output power measured during six months in a TV/FM station. There are 13 frequencies in FM and UHF frequency bands in pilot broadcast station. The analysis of results were carried out using data mining techniques. In addition, a prediction model on the basis of a Neural Network is identified. The electric field is affected by distance between the antenna and the receiver point, transmitters' output power and meteorological constituents of air pressure, temperature and humidity. The meteorological parameters and transmitters' power are used as inputs and the electric field is used as the output. After data acquisition, preprocessing is performed and the Neural Network of a multilayer perceptron model is applied. In addition, Multi Linear Regression is performed. In evaluation, the performance of the proposed techniques is based on the root mean square error (RMSE) property. The least MSE obtained for the proposed model based on Neural Network amounted to 0.198 while the least MSE of Regression was 0.280. The results showed that for a given input of the atmospheric parameters as well as the transmitter power, the intensity of electric field can be predicted as well as the determining the relationship between the atmospheric parameters, transmitters' power and electric field strength. The statistical and correlation analysis used to assess the relation between each parameter and signal strength concluded that the temperature and wind direction have an inverted linear relationship with the signal level while the others have a direct linear relationship.


Assuntos
Meteorologia , Mineração de Dados , Monitoramento Ambiental , Modelos Lineares , Vento
15.
Sci Total Environ ; 697: 134020, 2019 Dec 20.
Artigo em Inglês | MEDLINE | ID: mdl-31491629

RESUMO

The 16S rRNA gene metabarcoding approach has been used to characterize the structure of the airborne bacterial community of PM10 samples, and investigate the dependence on meteorology, seasons, and long-range transported air masses. The PM10 samples were collected at a Central Mediterranean coastal site, away from large sources of local pollution. Proteobacteria, Cyanobacteria, Actinobacteria, Firmicutes, and Bacteroidetes, which were found in all samples, were the most abundant phyla. Calothrix, Pseudomonas, and Bacillus were the most abundant genera. The within-sample relative abundance (RA) of each phylum/genus varied from sample to sample. Calothrix was the most abundant genus during the advection of desert dust and Atlantic air masses, Pseudomonas was the most abundant genus when the advected air flows spent several hours over lands or close to lands affected by anthropogenic activities, before reaching the study site. The bacterial community richness and biodiversity of the PM10 samples on average increased from winter to spring, while the sample dissimilarity on average decreased from winter to spring. The spring meteorological conditions over the Mediterranean, which have likely contributed to maintain for longer time the bacterial community in the atmosphere, could have been responsible for the above results. The analysis of the presumptive species-level characterization of the airborne bacterial community has revealed that the abundance of human (opportunistic) pathogens was highly inhomogeneous among samples, without any significant change from winter to spring. We also found that the PM10 samples collected during the advection of desert dust and Atlantic air masses were on average the less enriched in human (opportunistic) pathogenic species.


Assuntos
Microbiologia do Ar , Poluentes Atmosféricos/análise , Monitoramento Ambiental , Movimentos do Ar , Bactérias , Mar Mediterrâneo , Meteorologia
16.
Environ Sci Pollut Res Int ; 26(33): 34357-34367, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31493079

RESUMO

To investigate the acid-extractable heavy metals in fine particulate matter (PM2.5) over Xi'an, China, 24-h PM2.5 samples were collected every 3 days from December 2015 through November 2016. The bioavailable fraction, termed here the bioavailability index (BI), of PM2.5-bound metal (As, Ba, Cd, Co, Cu, Mn, Ni, Pb, Ti, V, and Zn) and potential influencing factors, including relative humidity, temperature, air pressure, wind speed, visibility, PM2.5, and SO2 concentrations, were assessed in this study. The annual average PM2.5 concentration was 50.6 ± 35.6 µg m-3, 1.5 times higher than the Chinese national secondary standard. Zn, Ti, and As were the most abundant elements of those analyzed in the PM2.5 samples, accounting for 72.1% of total quantity. The seasonal variations and enrichment factor analysis of heavy metals revealed that coal combustion in winter was a crucial source of Pb, Co, Cu, and Zn; and dust resuspension in spring contributed considerable Mn, Ti, and V. The acid-extractable fractions of the measured metals varied. Pb, Cu, Mn, and Zn exhibited relatively high acid-extractable concentrations and BI values. Pb was mostly in the acid-extractable fraction in PM2.5, with a mean BI value of 66.7%, the highest in summer (69.8%) and lowest in winter (63.7%). Moreover, the BIs of PM2.5-bound heavy metals were inversely related to temperature and wind speed, whereas positively correlated with relative humidity, SO2, and PM2.5 concentration in this study. This study assessed the seasonal distribution and meteorological influence of acid-extractable heavy metals, providing a deeper understanding of atmospheric heavy metal pollution in Xi'an, China.


Assuntos
Poluentes Atmosféricos/química , Poluição do Ar/estatística & dados numéricos , Monitoramento Ambiental , Metais Pesados/química , Conceitos Meteorológicos , Material Particulado/química , Ácidos/análise , Poluentes Atmosféricos/análise , Disponibilidade Biológica , China , Poeira/análise , Metais Pesados/análise , Meteorologia , Material Particulado/análise , Estações do Ano
17.
Environ Pollut ; 254(Pt A): 112952, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31369913

RESUMO

We have carried out a comprehensive analysis of six air pollutants (particles with an aerodynamic diameter less than 2.5 µm (PM2.5) and less than 10 µm (PM10), carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3)) in western China, including the spatiotemporal characteristics of air pollutants, their relationship with meteorological factors and emission sources, and the efficiency of emission control strategies for the region. Based hourly observations at 23 sites in western China from June 2016 to May 2017, concentrations of most pollutants were higher outside the Tibetan Plateau, lowest in summer and highest in winter, the exception being O3. This was partially because meteorological conditions in winter were found to the most unfavorable to pollutant dispersion and dilution than other seasons. Pollutant concentrations at most sites were correlated with the residential emissions which were higher in winter, but anti-correlated with the industrial emissions which were lower during the winter holiday period. The Weather Research and Forecasting with Chemistry (WRF-Chem) simulations of four pollution control strategies indicated that reduction of residential emissions is crucial to alleviate PM2.5, PM10, and CO pollution in western China, although reduction of industrial and transport emissions can reduce SO2 and NO2, respectively. Since PM2.5 and PM10 were also found to be the species most and next frequently responsible for extremely serious pollution in western China, respectively, we recommend pollution control regulations that target residential emissions.


Assuntos
Poluentes Atmosféricos/análise , Poluição do Ar/estatística & dados numéricos , Poluição do Ar/análise , Monóxido de Carbono/análise , China , Habitação , Conceitos Meteorológicos , Meteorologia , Dióxido de Nitrogênio/análise , Ozônio/análise , Material Particulado/análise , Estações do Ano , Dióxido de Enxofre/análise , Tempo (Meteorologia)
18.
J Environ Manage ; 248: 109333, 2019 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-31398677

RESUMO

While the economy and society of China are developing rapidly, various ecological and environmental problems continue to emerge. The problem of air pollution is becoming an issue of increasing concern. Since 2013, Beijing, Tianjin, and Hebei Provinces have collaborated to control air pollution and have achieved certain success. However, the air pollution problem in the capital, Beijing, remains severe. In this study, a quantitative evaluation and analysis method is established for assessing urban air quality and its influencing factors by developing a driving force model. The model combines multiple regression and principal component analysis to account for social, economic, meteorological, and regional factors. The influencing factors with the highest contributions are regional transmission, energy consumption, and industrial structure. This model is used to project the future trend of the air quality in Beijing based on the evolution of the influencing factors, aimed to provide a theoretical basis for policies improving Beijing's air quality.


Assuntos
Poluição do Ar , Meteorologia , China , Ecologia
19.
Environ Pollut ; 254(Pt A): 113026, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31419658

RESUMO

The data of most toxic particulate pollutants (PM2.5 and PM10) obtained from a dense SAFAR observational network in four Indian mega cities (Delhi, Mumbai, Pune and Ahmedabad) located in North-West part of India, not very far from each other, have been presented in this work. In spite of similar kind of sources of anthropogenic local emissions, each city has its unique air pollution footprints. The paper addresses the role of geographical location based prevailing meteorology in determining the variability of particulate matter in different seasons and processes responsible for the same. We hereby demonstrate that although Delhi has the highest level of particulate matters, the percentage share of PM2.5 in PM10 is highest for Mumbai (60%) as compared to 50% of Delhi. The pollutant levels of Delhi, Mumbai and Pune show strong seasonal variability whereas Ahmedabad does not show any significant variation for summer to winter. We have further discussed that the landlocked geography of Delhi and coastal location of Mumbai often play a dominant role in the distribution of air pollutants. Hence, the mitigation options require specific consideration of integrated approach for each city.


Assuntos
Poluentes Atmosféricos/análise , Poluição do Ar/prevenção & controle , Monitoramento Ambiental , Material Particulado/análise , Poluição do Ar/análise , Poluição do Ar/estatística & dados numéricos , Cidades , Poeira/análise , Índia , Meteorologia , Estações do Ano
20.
Environ Pollut ; 254(Pt A): 113025, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31419660

RESUMO

The present study utilizes 18 years of long-term (2001-2018) data collected from six active AERONET sites over the Indo-Gangetic Plain (IGP) and the North China Plain (NCP) areas in Southeast Asia. The annual mean (±SD) aerosol optical thickness at 440 nm (AOT440) was found high at XiangHe (0.92 ±â€¯0.69) and Taihu (0.90 ±â€¯0.51) followed by Beijing (0.81 ±â€¯0.69), Lahore (0.81 ±â€¯0.43), and Kanpur (0.73 ±â€¯0.35) and low at Karachi (0.52 ±â€¯0.23). Seasonally, high AOT440 with corresponding high Ångström exponent (ANG440-870) noticed during JJA for all sites, except Kanpur, suggesting the dominance of fine-mode particles, generally associated with large anthropogenic emissions. Climatologically, an increasing (decreasing) trend was observed over IGP (NCP) sites, with the highest (lowest) percentage of departures in AOT440 found over Beijing (Karachi). We further identified major aerosol types which showed the dominance of biomass burning, urban-industrial followed by the mixed type of aerosols. In addition, single scattering albedo (SSA), asymmetry parameter (ASP), volume size distribution (VSD), and complex aerosol refractive index (RI) showed significant temporal and spectral changes, illustrating the complexity of aerosol types. At last, the annual mean direct aerosol radiative forcing at the top, bottom, and within the atmosphere for all sites were found in the range from -17.36 ±â€¯3.75 to -45.17 ±â€¯4.87 W m-2, -64.6 ±â€¯4.86 to -93.7 ±â€¯10.27 W m-2, and 40.5 ±â€¯6.43 to 68.25 ±â€¯7.26 W m-2, respectively, with an averaged atmospheric heating rate of 0.9-2.3 K day-1. A large amount of anthropogenic aerosols showed a significant effect of heating (cooling) on the atmosphere (surface) results obviously, due to an increased rate of atmospheric heating. Therefore, the thermodynamic effects of anthropogenic aerosols on the atmospheric circulation and its structure should be taken into consideration for future study over the experimental sites.


Assuntos
Aerossóis/análise , Poluentes Atmosféricos/análise , Monitoramento Ambiental/métodos , Ásia Sudeste , Atmosfera/química , Pequim , Biomassa , China , Meteorologia
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