AQI time series prediction based on a hybrid data decomposition and echo state networks

Environmental Science and Pollution Research - A hybrid AQI time series prediction model is proposed based on EWT-SE-VMD secondary decomposition, ICA (imperialist competitive algorithm) feature...     

Autoregressive models in environmental forecasting time series: a theoretical and application review

Environmental Science and Pollution Research - Though globalization, industrialization, and urbanization have escalated the economic growth of nations, these activities have played foul on the...     

The COVID-19 pandemic: prediction study based on machine learning models

Environmental Science and Pollution Research - COVID-19 was first discovered in Wuhan, China in December 2019. It is one of the worst pandemics in human history. Recent studies reported that...     

Risk assessment and validation of farmland abandonment based on time series change detection

Environmental Science and Pollution Research - Farmland abandonment, a widespread phenomenon during land-use transition, leads to a cycling or vanishing evolution of farmland resources. As...     

Unsupervised monitoring of vegetation in a surface coal mining region based on NDVI time series

Environmental Science and Pollution Research - Surface coal mining causes vegetation disturbance while providing an energy source. Thus, much attention is given to monitoring the vegetation of...     

上海城市化对气象要素和臭氧浓度的影响

为探讨城市化引起的土地利用变化对上海近地面气象要素和臭氧(O3)浓度的影响,运用美国国家大气研究中心等机构共同开发的WRF-Chem模式,在考虑扩大城市用地、运用城市冠层模式以及城市人为热影响的基础上,针对上海地区2个不同发展时期的下垫面土地利用类型,就2007年3次高浓度O3天气过程,设置4组灵敏性试验进行模拟。结果表明,以虹桥机场站为代表的市区受城市化影响温度升高、相对湿度降低、风速减小,日平均温度最高上升3.5℃,日平均相对湿度最大降低20%,日平均风速最大减小1.5m/s;但以青浦站和川沙站为代表的郊区受城市化影响不明显。此外,以卢湾站为代表的市区,O3浓度普遍增加,日均值最高可增加8.3μg/m3;但以川沙站和淀山湖站为代表的郊区,O3浓度的变化随着个例的不同有增加也有减少。     

An improved trend vegetation analysis for non-stationary NDVI time series based on wavelet transform

Environmental Science and Pollution Research - The aim of this paper is to improve trend analysis for non-stationary Normalized Difference Vegetation Index (NDVI) time series (TS) over different...     

Weather integrated malaria prediction system using Bayesian structural time series model for northeast states of India

Environmental Science and Pollution Research - Malaria is an endemic disease in India and targeted to eliminate by the year 2030. The present study is aimed at understanding the epidemiological...     

An investigation of distributed lag models in the context of air pollution and mortality time series analysis

In particulate air pollution mortality time series studies, the particulate air pollution exposure measure used is typically the current day's or the previous day's air pollution concentration or a multi-day moving average air pollution concentration. Distributed lag models (DLMs) that allow for differential air pollution effects that are spread over multiple days are seen as an improvement over using a single- or multi-day moving average air pollution exposure measure. However, at the current time, the statistical properties of DLMs as a measure of air pollution exposure have not been investigated. In this paper, a simulation study is used to investigate the performance of DLMs as a measure of air pollution exposure in comparison with single- and multi-day moving average air pollution exposure measures under various forms for the true effect of air pollution on mortality. The simulation study shows that DLMs offer a more robust measure of the effect of air pollution on mortality and avoid the potential for a large negative bias compared with single- or multi-day moving average air pollution exposure measures. This is important information. In many U.S. cities, particulate air pollution concentrations are observed only once every six days, meaning it is often only possible to use single-day particulate air pollution exposure measures. The results from this paper will help quantify the magnitude of the negative bias that can result from using single-day exposure measures. The implications of this work for future air pollution mortality time series studies are discussed. The data used in this paper are concurrent daily time series of mortality, weather, and particulate air pollution from Cook County, IL, for the period 1987-1994.     

Estimating risk at a Superfund site using passive sampling devices as biological surrogates in human health risk models

Passive sampling devices (PSDs) sequester the freely dissolved fraction of lipophilic contaminants, mimicking passive chemical uptake and accumulation by biomembranes and lipid tissues. Public Health Assessments that inform the public about health risks from exposure to contaminants through consumption of resident fish are generally based on tissue data, which can be difficult to obtain and requires destructive sampling. The purpose of this study is to apply PSD data in a Public Health Assessment to demonstrate that PSDs can be used as a biological surrogate to evaluate potential human health risks and elucidate spatio-temporal variations in risk. PSDs were used to measure polycyclic aromatic hydrocarbons (PAHs) in the Willamette River; upriver, downriver and within the Portland Harbor Superfund megasite for 3 years during wet and dry seasons. Based on an existing Public Health Assessment for this area, concentrations of PAHs in PSDs were substituted for fish tissue concentrations. PSD measured PAH concentrations captured the magnitude, range and variability of PAH concentrations reported for fish/shellfish from Portland Harbor. Using PSD results in place of fish data revealed an unacceptable risk level for cancer in all seasons but no unacceptable risk for non-cancer endpoints. Estimated cancer risk varied by several orders of magnitude based on season and location. Sites near coal tar contamination demonstrated the highest risk, particularly during the dry season and remediation activities. Incorporating PSD data into Public Health Assessments provides specific spatial and temporal contaminant exposure information that can assist public health professionals in evaluating human health risks.     

Influence of meteorological factors on air pollution concentration for a coastal region in India

A long-term study of measurement of concentration of NO_x, SO₂ and TSP pollutants have been done in a port and harbour region in India. Monthly measurements of gaseous and particulate pollutants were made at six monitoring stations from January 1997 to December 2000. Meteorological data was also simultaneously collected. In this study, the relationship between monitored ambient air quality data and meteorological factors, such as wind speed, temperature, is statistically analysed, using the SPSS package. The monthly mean concentrations of NO_x, SO₂ and TSP were in the range of 19.5–59.0 μg/m3, 8.6–51.3 μg/m3 and 88.2–199.3 μg/m3, respectively. The results show that TSP is strongly correlated with NO_x and SO₂ with a correlation coefficient of 0.83 and 0.82, respectively. The correlation coefficients for TSP, NO_x, and SO₂ with wind are –0.78, –0.78, and –0.88, respectively.     

Improvement of health risk factors after reduction of VOC concentrations in industrial and urban areas

After reductions of fugitive and diffuse emissions by an industrial complex, a follow-up study was performed to determine the time variability of volatile organic compounds (VOCs) and the lifetime cancer risk (LCR). Passive samplers (3 M monitors) were placed outdoors (n?=?179) and indoors (n?=?75) in industrial, urban, and control areas for 4 weeks. Twenty-five compounds including n-alkanes, cycloalkanes, aromatics, chlorinated hydrocarbons, and terpenes were determined by GC/MS. The results show a significant decrease of all VOCs, especially in the industrial area and to a lesser extent in the urban area. The median outdoor concentration of benzene in the industrial area declined compared to the former study, around 85 % and about 50 % in the urban area, which in the past was strongly influenced by industrial emissions. Other carcinogenic compounds like styrene and tetrachloroethylene were reduced to approximately 60 %. VOC concentrations in control areas remained nearly unchanged. According to the determined BTEX ratios and interspecies correlations, in contrast to the previous study, traffic was identified as the main emission source in the urban and control areas and showed an increased influence in the industrial area. The LCR, calculated for benzene, styrene, and tetrachloroethylene, shows a decrease of one order of magnitude in accordance to the decreased total VOC concentrations and is now acceptable according to values proposed by the World Health Organization.     

A time series analysis of environmental and metrological factors impact on cutaneous leishmaniasis incidence in an endemic area of Dehloran,Iran

The aim of this study was to investigate the relationship between the environmental and metrological variables and cutaneous leishmaniasis (CL) transmission and its prediction in a region susceptible to this disease prevalence using a time series model. The accurate locations of 4437 CL diagnosed from 2011 to 2015 were obtained to be used in the time series model. Temperature, number of days with temperature over 30 °C, and number of earthquake were related to CL incidence using the Seasonal Auto-correlated Integrated Moving Average (SARIMA) model according to the Box-Jenkins method. In addition, the relationship between land use and surface soil type in 500- and 1000-m radius around the CL patients were investigated. The SARIMA models showed significant associations between environmental and meteorological variables and CL incidence adjusted for seasonality and auto-correlation. The result indicated that there are need more robust preventive programs in earthquake-prone areas with high temperature and inceptisol soil type than other areas. In addition, the region with these characteristics should be considered as high-risk areas for CL prevalence.     

Characteristics of Asian dust transport based on synoptic meteorological analysis over Korea

Classification of synoptic patterns and their correlation with dust events over East Asia were performed by means of cluster analysis. The average linkage and K-means clustering techniques were used to identify two major weather types during Asian dust events (ADEs; total 26 ADEs with 47 dusty days) of six spring seasons from 1996 to 2001. The first weather type mainly influenced neighboring Asian countries and frequently occurred with ADEs (approximately 23% of ADE cases). It mostly occurred under a surface high (low)-pressure system over the west (east) of the Korean peninsula coupled with an upper-level trough and cutoff low passage over the center of the Korean peninsula. It showed strong advection in the middle/ upper troposphere with both a high aerosol index and enhanced coarse particulate matter (PM) loading over Korea. In contrast, the second weather type was mostly associated with long distance or continental-scale transport and occurred less frequently with ADEs (approximately 15%). It appeared with an upper-level trough and a cutoff low vertically connected with a surface low system that was formed by a strong cyclonic vortex over the north of the Korean peninsula. There were weak advection, low aerosol index, and low coarse PM concentration over the Korean peninsula during the second weather type. In addition, it was found to be mostly associated with the trans-Pacific transport of Asian dust to the western coast of North America.     

Extreme value modeling for the analysis and prediction of time series of extreme tropospheric ozone levels: a case study

The occurrence of high concentrations of tropospheric ozone is considered as one of the most important issues of air management programs. The prediction of dangerous ozone levels for the public health and the environment, along with the assessment of air quality control programs aimed at reducing their severity, is of considerable interest to the scientific community and to policy makers. The chemical mechanisms of tropospheric ozone formation are complex, and highly variable meteorological conditions contribute additionally to difficulties in accurate study and prediction of high levels of ozone. Statistical methods offer an effective approach to understand the problem and eventually improve the ability to predict maximum levels of ozone. In this paper an extreme value model is developed to study data sets that consist of periodically collected maxima of tropospheric ozone concentrations and meteorological variables. The methods are applied to daily tropospheric ozone maxima in Guadalajara City, Mexico, for the period January 1997 to December 2006. The model adjusts the daily rate of change in ozone for concurrent impacts of seasonality and present and past meteorological conditions, which include surface temperature, wind speed, wind direction, relative humidity, and ozone. The results indicate that trend, annual effects, and key meteorological variables along with some interactions explain the variation in daily ozone maxima. Prediction performance assessments yield reasonably good results.     

Variation of surface ozone in Campo Grande,Brazil: meteorological effect analysis and prediction

The effect of meteorological variables on surface ozone (O₃) concentrations was analysed based on temporal variation of linear correlation and artificial neural network (ANN) models defined by genetic algorithms (GAs). ANN models were also used to predict the daily average concentration of this air pollutant in Campo Grande, Brazil. Three methodologies were applied using GAs, two of them considering threshold models. In these models, the variables selected to define different regimes were daily average O₃ concentration, relative humidity and solar radiation. The threshold model that considers two O₃ regimes was the one that correctly describes the effect of important meteorological variables in O₃ behaviour, presenting also a good predictive performance. Solar radiation, relative humidity and rainfall were considered significant for both O₃ regimes; however, wind speed (dispersion effect) was only significant for high concentrations. According to this model, high O₃ concentrations corresponded to high solar radiation, low relative humidity and wind speed. This model showed to be a powerful tool to interpret the O₃ behaviour, being useful to define policy strategies for human health protection regarding air pollution.     

Effect of shoreline meteorological measurements on NOAA Buoy model prediction of coastal air–sea gas transfer

The NOAA Buoy model is currently used to estimate the air–sea transfer rates of highly soluble gases over coastal water bodies, such as Tampa Bay, using offshore meteorological measurements. Since a goal of the BRACE study was to improve estimates of nitrogen deposition over Tampa Bay, our objective was to investigate if the model accurately predicts gas transfer when shoreline input data are used in lieu of offshore measurements. To accomplish this objective, we compared over-water measurements of sensible heat with NOAA Buoy model predictions using both offshore and shoreline meteorology. In the summer months, the apparent daytime influence of land surface heating on air temperature produces a higher air than water temperature at the shoreline. For the NOAA Buoy model, this yields stable atmospheric conditions and thus under-predicts the over-water exchange rates for a shallow estuary. If the data records are removed from the model for periods when air temperature is 4.8 K greater than the water temperature, the shoreline and over-water transfer rates are in reasonable agreement.     

Evaluating the use of outputs from comprehensive meteorological models in air quality modeling applications

Currently used dispersion models, such as the AMS/EPA Regulatory Model (AERMOD), process routinely available meteorological observations to construct model inputs. Thus, model estimates of concentrations depend on the availability and quality of meteorological observations, as well as the specification of surface characteristics at the observing site. We can be less reliant on these meteorological observations by using outputs from prognostic models, which are routinely run by the National Oceanic and Atmospheric Administration (NOAA). The forecast fields are available daily over a grid system that covers all of the United States. These model outputs can be readily accessed and used for dispersion applications to construct model inputs with little processing. This study examines the usefulness of these outputs through the relative performance of a dispersion model that has input requirements similar to those of AERMOD. The dispersion model was used to simulate observed tracer concentrations from a Tracer Field Study conducted in Wilmington, California in 2004 using four different sources of inputs: (1) onsite measurements; (2) National Weather Service measurements from a nearby airport; (3) readily available forecast model outputs from the Eta Model; and (4) readily available and more spatially resolved forecast model outputs from the MM5 prognostic model. The comparison of the results from these simulations indicate that comprehensive models, such as MM5 and Eta, have the potential of providing adequate meteorological inputs for currently used short-range dispersion models such as AERMOD.     

Probabilistic health risk assessment based on Monte Carlo simulation for pesticide residues in date fruits of Iran

Environmental Science and Pollution Research - The validation of an analytical procedure based on the quick, easy, cheap, effective, rugged, and safe (QuEChERS) approach is presented for...     

广州PM2.5污染特征及与气象因子的关系分析

基于2014—2016年广州PM_(2.5)浓度逐时观测数据,研究了广州PM_(2.5)污染变化特征及其与气象因子的关系,确定了影响广州大气能见度的PM_(2.5)浓度阈值。结果表明:(1)2014—2016年广州PM_(2.5)质量浓度平均为32.7μg/m3,广州1月PM_(2.5)污染最重,轻度、中度、重度污染频率合计达20.16%;(2)PM_(2.5)浓度与风速、降水、气温、能见度呈负相关,与相对湿度、气压呈正相关;(3)广州地区在南风的条件下PM_(2.5)浓度最低,风速小于2m/s的偏北风下易出现污染;(4)PM_(2.5)浓度与相对湿度共同影响广州能见度的变化,随着相对湿度的增加,PM_(2.5)浓度的敏感阈值不断减小,通常当PM_(2.5)高于37.3μg/m3时,控制PM_(2.5)对改善城市能见度成效相对缓慢,而当PM_(2.5)浓度低于此阈值时,降低PM_(2.5)将显著提高大气能见度。