Stochastic modeling of atmospheric pollution: a spatial time-series framework. Part II: application to monitoring monthly sulfate deposition over Europe

A spatial time-series framework is adopted for stochastic modeling of monthly averaged sulfate deposition over Europe. The sulfur concentration data used in this study were measured at the European Monitoring and Evaluation Program (EMEP) monitoring network from January 1980 to December 1988. Parametric temporal trend and residual models, associated with long-term (linear trend or annual periodicity) and short-term (seasonal) concentration variability, respectively, are first established at monitoring stations. The resulting model parameters are regionalized in space to arrive at parametric trend and residual models at any unmonitored location. Stochastic simulation is performed for prediction and modeling of joint uncertainty regarding unknown sulfur concentration levels at unmonitored spatial locations and time instants. The case study illustrates the applicability of the proposed spatial time series framework to a real-world data set.     

城市供水管网二次加氯研究进展

传统城市供水管网一般在净水厂实行消毒以减少用户受到病原体及各种水传播疾病的危害.既要保证足够的氯残留,又要减少过量氯产生的气味和可能的消毒副产物(DBPs),传统的消毒方法经常是不可能的.为了解决这对矛盾,二次加氯将会是个很好的解决策略.二次加氯的优势已经吸引了国内外许多专家学者的关注,对二次加氯的研究进展进行了综合的论述,从新的角度对现有的优化模型进行了分类,并比较和推荐了认为可行或相对较好的模型.     

The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies

A methodology is developed to include wind flow effects in land use regression (LUR) models for predicting nitrogen dioxide (NO₂) concentrations for health exposure studies. NO₂ is widely used in health studies as an indicator of traffic-generated air pollution in urban areas. Incorporation of high-resolution interpolated observed wind direction from a network of 38 weather stations in a LUR model improved NO₂ concentration estimates in densely populated, high traffic and industrial/business areas in Toronto-Hamilton urban airshed (THUA) of Ontario, Canada. These small-area variations in air pollution concentrations that are probably more important for health exposure studies may not be detected by sparse continuous air pollution monitoring network or conventional interpolation methods. Observed wind fields were also compared with wind fields generated by Global Environmental Multiscale-High resolution Model Application Project (GEM-HiMAP) to explore the feasibility of using regional weather forecasting model simulated wind fields in LUR models when observed data are either sparse or not available. While GEM-HiMAP predicted wind fields well at large scales, it was unable to resolve wind flow patterns at smaller scales. These results suggest caution and careful evaluation of regional weather forecasting model simulated wind fields before incorporating into human exposure models for health studies. This study has demonstrated that wind fields may be integrated into the land use regression framework. Such integration has a discernable influence on both the overall model prediction and perhaps more importantly for health effects assessment on the relative spatial distribution of traffic pollution throughout the THUA. Methodology developed in this study may be applied in other large urban areas across the world.     

Air Pollution Patterns In New York City

Some preliminary analyses of data selected from three years of smoke shade and sulfur dioxide measurements from the forty air monitoring stations in New York City are presented. The purpose of these analyses is to investigate the spatial-temporal variation in concentration of these pollutants throughout the five boroughs of the city. Air pollution health effects studies in New York City have often used city-wide daily morbidity or mortality statistics and related them to air pollution levels obtained from a single monitoring station. The question of whether readings at one station in New York City can adequately represent the air pollution exposure for the population in the five boroughs is examined in this paper. Some samples of correlation matrices of daily pollution averages obtained from the forty air monitoring stations are presented to illustrate the day-to-day variation in pollution in various sections of New York City. It was found that interstation correlations are not high enough to justify the use of one central pollution measuring station as representative of a large metropolitan area. Sulfur dioxide correlates better between stations than smoke shade; this may reflect the different nature and spatial distribution of sources of the two pollutants. Close proximity of stations, or the fact that they were at similar heights above street or sea level did not necessarily lead to higher correlation coefficients.     

Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.     

Urban air pollution monitoring and correlation properties between fixed-site stations

The rich regional air-monitoring network of the Emilia-Romagna region of Italy has been used to quantify the spatial variability of the main pollutants within urban environments and to analyze the correlations between stations. The spatial variability of the concentrations of the majority of pollutants within the city was very high, making it difficult to differentiate and characterize the urban environments and to apply legal limits with uniform criteria. On the other hand, the correlations between the fixed-site monitoring stations were high enough for their data to be retained generally very appropriately for controlling temporal trends. Starting from the high correlation level, a procedure was proposed and tested to derive pollution levels, using short-term measurements, such as passive samplers and mobile-station data. The importance of long-term statistics in urban air pollution mapping was emphasized. Treatment of missing data in time series and quality assurance were indicated as possible fields for applications for the correlation properties.     

Modeling the space/time distribution of particulate matter in Thailand and optimizing its monitoring network

The space/time distribution of PM₁₀ in Thailand is modeled using the Bayesian maximum entropy (BME) method of modern spatiotemporal geostatistics. Three kinds of BME spatiotemporal maps over Thailand are sought on the most polluted day for each year of a 6-year period from 1998 to 2003. These three maps are (1) the map of the BME estimate of daily PM₁₀, (2) the map of the associated BME prediction error, and (3) the BME non-attainment map showing areas where the BME estimate does not attain a 68% probability of meeting the ambient standard for PM₁₀. These detailed space/time PM₁₀ maps provide invaluable information for decision-makers in air quality management. Knowing accurately the spatiotemporal distribution of PM₁₀ is necessary to develop and evaluate strategies used to abate PM₁₀ levels. The space/time BME estimate of PM₁₀ on the worst day of the year offers a general picture as to where daily PM₁₀ levels are not in compliance with the air-quality standard. Delineating these areas leads to the BME non-attainment maps, which are useful in identifying unhealthy zones, where sensitive population such as asthmatic children, seniors, or those with cardiopulmonary disease should be advised to avoid outdoor activities. The results of the space/time BME analysis of PM₁₀ are further extended to assess whether the current monitoring network is adequate. The current distribution of monitoring stations can be evaluated by combining the available demographic information with the BME estimation error maps. Administrative districts with large population size and high BME normalized estimation error are suggested as the target for adding new monitoring stations.     

The effect of air pollution and other environmental stressors on leaf fluctuating asymmetry and specific leaf area of Salix alba L

We aimed at evaluating the effect of low-level air pollution on leaf area fluctuating asymmetry (FAA) and specific leaf area (SLA) of Salix alba L., taking into account other environmental factors. Cuttings were grown in standardized conditions in the near vicinity of air quality measuring stations in Belgium. Variability of SLA and FAA between measuring stations explained 83% and 7.26%, respectively, of the total variability. FAA was not influenced by air pollution or environmental factors such as shading, herbivory, air temperature and humidity. SLA was increased by an increase in shadow, while NO_x and O₃ concentrations had only a marginal influence. The influence of SO₂ concentration was negligible. Although our data analysis suggests a relationship between SLA and NO_x/O₃ concentration, the absence of a straightforward relationship between FAA and SLA and air pollution still questions the usefulness of these bio-indicators for monitoring air pollution.     

Spatial variability of sydney air quality by cumulative semivariogram

This paper establishes that an isotropic spatial correlation function in the form of a modified Bessel function of the second kind, first order, can be used to model the spatial variability of a pollution concentration field over a sufficiently long period of time in which the variability due to meteorological factors has been smoothed out. The corresponding cumulative semivariogram is derived and fitted by nonlinear least-squares to monthly averaged ozone data at 18 monitoring stations of the Sydney region. The good fit of the model indicates that the Sydney airshed has homogeneous and isotropic subregions whose radius of influence is about 17 km. The Bessel function form of the spatial correlation has a physical meaning as it is derived from the diffusion equation; hence, it is expected that the model can be used, in general, to represent the spatial variability of a smoothed homogeneous and isotropic concentration field.     

Spatial variation of airborne pollutant concentrations in Brisbane,Australia and its potential impact on population exposure assessment

Spread of air pollution sources and non-uniform mixing conditions in urban or regional air sheds often result in spatial variation of pollutant concentrations over different parts of the air sheds. A comprehensive understanding of this variation of concentrations is imperative for informed planning, monitoring and assessment in a range of critical areas including assessment of monitoring network efficiency or assessment of population exposure variation as a function of the location in the city. The aims of this work were to study the citywide variability of pollutants as measured by “urban background” type monitoring stations and to interpret the results in relation to the applicability of the data to population exposure assessments and the network efficiency. A comparison between ambient concentrations of NO_x, ozone and PM₁₀ was made for three stations in the Brisbane air shed network. The best correlated between the three stations were ozone concentrations followed by NO_x concentration, with the worst correlations observed for PM₁₀. With a few exceptions correlations of all pollutants between the stations were statistically significant. Marginally better were the correlations for the lower concentrations of pollutants that represent urban background, over the correlations for higher concentrations, representing peak values. Implications of these findings on application of the monitoring data to air-quality management, as well as the need for further investigations has been discussed.     

Using a chemistry transport model to account for the spatial variability of exposure concentrations in epidemiologic air pollution studies

Environmental epidemiology and more specifically time-series analysis have traditionally used area-averaged pollutant concentrations measured at central monitors as exposure surrogates to associate health outcomes with air pollution. However, spatial aggregation has been shown to contribute to the overall bias in the estimation of the exposure-response functions. This paper presents the benefit of adding features of the spatial variability of exposure by using concentration fields modeled with a chemistry transport model instead of monitor data and accounting for human activity patterns. On the basis of county-level census data for the city of Paris, France, and a Monte Carlo simulation, a simple activity model was developed accounting for the temporal variability between working and evening hours as well as during transit. By combining activity data with modeled concentrations, the downtown, suburban, and rural spatial patterns in exposure to nitrogen dioxide, ozone, and PM2.5 (particulate matter [PM] < or = 10 microm in aerodynamic diameter) were captured and parametrized. Exposures predicted with this model were used in a time-series study of the short-term effect of air pollution on total nonaccidental mortality for the 4-yr period from 2001 to 2004. It was shown that the time series of the exposure surrogates developed here are less correlated across co-pollutants than in the case of the area-averaged monitor data. This led to less biased exposure-response functions when all three co-pollutants were inserted simultaneously in the same regression model. This finding yields insight into pollutant-specific health effects that are otherwise masked by the high correlation among co-pollutants.     

Biomarkers responses of the earthworm Eisenia fetida to acetochlor exposure in OECD soil

To examine the potential of a suite of biomarkers as early warning indicators of environmental pollution, sperm count, neutral red retention time (NRRT) and DNA damage were measured in earthworm Eisenia fetida exposed to increasing concentrations of acetochlor in OECD soil. The neutral red retention time of earthworms coelomocytes was sensitive to acetochlor pollution, and decreased significantly when the concentration was more than 10mgkg(-1) after 30 and 60 days of exposure (P<0.05). The reduced neutral red retention time correlated with the soil acetochlor residual. Sperm count decreased significantly at the concentrations of 40 and 80mgkg(-1) after 15 days of exposure (P<0.05). The DNA damage of earthworms coelomocytes increased significantly after 30 days of exposure at the highest concentration (80mgkg(-1); P<0.05). Earthworms were under physiological stress at field dose of acetochlor (10mgkg(-1)). Higher concentrations of acetochlor caused sperm count decrease and DNA damage of earthworms. Such a suite of biomarkers could serve as indicators of the health of the soil environment and to evaluate the toxicity of acetochlor on earthworms or as a means of monitoring soil acetochlor pollution.     

Assessment of traffic-related air pollution in two street canyons in Paris: implications for exposure studies

The small-scale spatial variability of air pollution observed in urban areas has created concern about the representativeness of measurements used in exposure studies. It is suspected that limit values for traffic-related pollutants may be exceeded near busy streets, although respected at urban background sites. In order to assess spatial concentration gradients and identify weather conditions that might induce air pollution episodes in urban areas, different sampling and modelling techniques were studied.Two intensive monitoring campaigns were carried out in typical street canyons in Paris during winter and summer. Steep cross-road and vertical concentration gradients were observed within the canyons, in addition to large differences between roadside and background levels. Low winds and winds parallel to the street axis were identified as the worst dispersion conditions. The correlation between the measured compounds gave an insight into their sources and fate. An empirical relationship between CO and benzene was established. Two relatively simple mathematical models and an algorithm describing vertical pollutant dispersion were used. The combination of monitoring and modelling techniques proposed in this study can be seen as a reliable and cost-effective method for assessing air quality in urban micro-environments. These findings may have important implications in designing monitoring studies to support investigation on the health effects of traffic-related air pollution.     

Spatial variations of ground level ozone concentrations in areas of different scales

The spatial variation of ground level ozone concentrations was investigated for areas of three different scales: (1) an air quality management district (a region about 100×70 km²) in northern Taiwan, (2) the neighborhood (about 2 km in radius) of an air quality monitoring station, and (3) an open field (about 400×600 m²) surrounded by 3- and 4-story buildings in an elementary school. Analysis of data on hourly ozone concentration, obtained at 13 m above the ground at 21 monitoring stations in the air quality management district, showed that the stations downwind of the urban center in the district had significantly higher ozone concentrations. Measurements for 8-h average ozone concentrations at 1.5 m above the ground by passive samplers showed that, in a flat area about 2 km in radius, the ratios of the ozone concentration at open areas to that at the monitoring station (0.86–0.93) were significantly higher than those obtained at areas with higher traffic flow and density of buildings (0.60–0.68). For the open field in an elementary school, the 8-h average ozone concentrations at 1.5 m above the ground at sites less than 10 m from the nearest building were considerably lower than those at sites farther away from buildings. The results indicated that, in areas of small scales, the spatial distributions of ozone concentration were highly non-uniform and there were appreciable day-to-day variability in spatial distribution. Such variability should be taken into account in determining the extent to which an individual is exposed to ozone.     

连云港市大气污染物时间序列的长程相关性分析

运用R/S分析(Rescaled Range Analysis)方法对连云港市3个大气环境定位监测点近10年来的SO2、NOx和TSP序列数据进行了时间序列的长程相关性分析.结果表明,连云港市SO2、NOx和TSP月均值序列的Hurst指数在0.500～1.000,表现出明显的长程相关性,并且这种相关性程度的强弱随着城市功能区的不同而表现出一定的差异.研究结果对于认识连云港市城市大气环境质量变化过程和科学制定环保决策具有重要意义.     

Assessment and rationalization of water quality monitoring network: a multivariate statistical approach to the Kabbini River (India)

The establishment of an efficient surface water quality monitoring (WQM) network is a critical component in the assessment, restoration and protection of river water quality. A periodic evaluation of monitoring network is mandatory to ensure effective data collection and possible redesigning of existing network in a river catchment. In this study, the efficacy and appropriateness of existing water quality monitoring network in the Kabbini River basin of Kerala, India is presented. Significant multivariate statistical techniques like principal component analysis (PCA) and principal factor analysis (PFA) have been employed to evaluate the efficiency of the surface water quality monitoring network with monitoring stations as the evaluated variables for the interpretation of complex data matrix of the river basin. The main objective is to identify significant monitoring stations that must essentially be included in assessing annual and seasonal variations of river water quality. Moreover, the significance of seasonal redesign of the monitoring network was also investigated to capture valuable information on water quality from the network. Results identified few monitoring stations as insignificant in explaining the annual variance of the dataset. Moreover, the seasonal redesign of the monitoring network through a multivariate statistical framework was found to capture valuable information from the system, thus making the network more efficient. Cluster analysis (CA) classified the sampling sites into different groups based on similarity in water quality characteristics. The PCA/PFA identified significant latent factors standing for different pollution sources such as organic pollution, industrial pollution, diffuse pollution and faecal contamination. Thus, the present study illustrates that various multivariate statistical techniques can be effectively employed in sustainable management of water resources. Highlights ? The effectiveness of existing river water quality monitoring network is assessed ? Significance of seasonal redesign of the monitoring network is demonstrated ? Rationalization of water quality parameters is performed in a statistical framework     

Characterizing the spatiotemporal variability of PM2.5 in Cusco,Peru using kriging with external drift

Advancing the understanding of spatiotemporal aspects of air pollution in the urban environment is an area where improved methods can be of great benefit to exposure assessment and policy support. This paper explores the potential of a technique known as kriging with external drift (KED) to provide high resolution maps of fine particulate matter for a downtown region of Cusco, Peru. There were three stages in this research. The first was to conduct a pilot level monitoring campaign to investigate ambient, regional, and street-level air pollutant concentrations for particulate matter (PM_2.5, PM₁₀) and carbon monoxide (CO) in the Province of Cusco. The second was to compile observations within a geographic information system (GIS) in order to characterize the proximal effect of the local transportation network, elevation, and land use classifications on PM_2.5. Third, regression, ordinary kriging and kriging with external drift were used to model PM_2.5 for three select time periods during a 24-h day. Statistical evaluations indicate kriging with external drift resulted in the strongest models explaining 64% of variability seen with morning particle concentrations, 25% for afternoon particles, and 53% in evening particles. These models capture spatial and temporal variability for air pollution in Cusco. These variations seem to be influenced, to varying degrees, by elevation, meteorological conditions, spatial location, and transportation characteristics. In conclusion, combining GIS, meteorological data and geostatistics proved to be a complementary suite of tools for incorporating spatiotemporal analysis into the air quality assessment.     

Real-time ozone mapping using a regression-interpolation hybrid approach, applied to Tucson, Arizona

Real-time ozone (O3) maps, intended for public access and mass media, are generated from spatially interpolating (i.e., kriging) sparse monitoring data and are typically characterized by over-smoothed surfaces that inadequately represent local-scale spatial patterns (e.g., averaged over 1 km2). In this paper, a hybrid regression-interpolation methodology is developed to enhance the representation of local-scale spatiotemporal patterns with an application to Tucson, Arizona. The mapping of local patterns is enhanced with pre-interpolation regression modeling of local-scale deviation-from-mean variability, preserving variation in the monitor data that is ubiquitous across the modeling domain (i.e., the areal mean). The model is trained on several years of deviation-from-mean hourly O3 data, and predictor variables are developed using theoretically and empirically derived proxy regression variables. The regression model explains a significant proportion of the variation in the data (r2 = 0.54), with an average error of 7.1 ppb. When augmented with the areal mean, the r2 of the pre-interpolation model increases to 0.847. Model residuals are then spatially interpolated to the extents of the modeling domain. Final concentration estimate maps are the summation of areal mean, regression, and spatially interpolated surfaces, preserving absolute values at monitor locations.     

GIS-based source identification and apportionment of diffuse water pollution: perfluorinated compound pollution in the Tokyo Bay basin

To efficiently reduce perfluorinated compound (PFC) pollution, it is important to have an understanding of PFC sources and their contribution to the pollution. In this study, source identification of diffuse water pollution by PFCs was conducted using a GIS-based approach. Major components of the source identification were collection of the monitoring data and preparation of the corresponding geographic information that was extracted from a constructed GIS database. The spatially distributed pollution factors were then explored by multiple linear regression analysis, after which they were visually expressed using GIS. Among the 35 PFC homologues measured in a survey of the Tokyo Bay basin, 18 homologues were analyzed. Pollution by perfluorooctane sulfonate (PFOS) was explained well by the percentage of arterial traffic area in the basin, and the 84% variance of the measured PFOS concentration was explained by two geographic variables, arterial traffic area and population. Source apportionment between point and nonpoint sources was conducted based on the results of the analysis. The contribution of PFOS from nonpoint sources was comparable to that from point sources in several major rivers flowing into Tokyo Bay. Source identification and apportionment using the GIS-based approach was shown to be effective, especially for ubiquitous types of pollution, such as PFC pollution.     

A climatology of regional background ozone at different elevations in Switzerland (1992–1998)

The ozone records of several monitoring stations in Switzerland from 1992 to 1998 are investigated with respect to the variability observed during regional background conditions, i.e. conditions with little detectable local or regional-scale influences as evident by NO_x and CO concentrations. The sites cover different altitudes between 490 and 3600 m asl. They are characteristic of near-surface conditions, the top of the planetary boundary layer or residual layer, the complex atmosphere in an alpine valley, and the free troposphere. The results reveal a distinctly different ozone variability (diurnal cycles, seasonal cycles, trends) during regional background conditions compared to all days. The estimated annual average ozone concentration under these conditions is between 33 and 50 ppb, dependent on altitude, with a spring maximum and an autumn/winter minimum. Differences in background ozone are found depending on the synoptic weather type. For all sites a positive ozone trend is calculated for background conditions that is larger than for all data. For the latter, the trends appear to be stronger positive for the last 7 years than for the last 11 years.