Proximity analysis of air pollution exposure and its potential risk

Proximity analysis and spatial variability of the ambient nitrogen dioxide (NO(2)) concentration in Rayong province, Thailand, were analyzed using geostatistics and spatial modeling techniques. Annual concentrations of nitrogen dioxide were predicted and spatially interpolated using various interpolation techniques (i.e. kriging, IDW and spline). Sensitivity analysis was carried out to assure the accuracy of the predicted results. The GIS-based exposure map was simulated and was assisted to identify high exposure areas. A health risk warning system was set for "action" (exceeds 100% of the annual average NO(2) guideline), for "alert" (between 66-100% of the annual average NO(2) guideline) and for "some concern" (between 33-66% of the annual average NO(2) guideline). Although no areas were exposed to an action level, many locations in the study area could have levels of "some concern". Potential risk to the population was analyzed by spatial interpolation of the nitrogen dioxide concentration with population data. The result indicated the number of people exposed to air pollution, as well as the areas which have a high risk to air pollution. About 88.3% of the total population in the study area live in areas where levels of air pollution are designated as being in the "some concern" zone. About 6.7% of the registered population have their residence in an area where action should be taken for air quality management. The study demonstrates the application of GIS-based prediction for the evaluation of exposure mapping, in order to determine the spatial extent and frequency of areas where pollution levels exceed target values, and their potential health impacts.     

Landslide susceptibility mapping in an area of underground mining using the multicriteria decision analysis method

Landslides are geomorphological phenomena that affect anthropogenic and natural features on the Earth’s surface. Many previous studies have identified several factors that have contributed to landslides. Among these factors are physical characteristics, such as slope, aspect, and land cover, of Earth’s surface. Moreover, landslides can be triggered by human activities such as underground mining. This study aims to identify landslide susceptibility areas by analyzing landslide-related factors, including land subsidence triggered by underground mining. The area of interest was Kozlu, Turkey, where underground mining has been in progress for the past 100 years. Thus, to identify landslide risk zones, the multicriteria decision analysis method, together with the analytical hierarchy method, was used. The datasets included were topography, land cover, geological settings, and mining-induced land subsidence. The spatial extent of land subsidence was estimated using a previously published model. A landslide susceptibility map (LSM) was developed using a purposely developed GIS-based software. The results were compared with a terrain deformation map, which was developed in a separate study using the differential synthetic aperture radar interferometry (DInSAR) technique. The results showed a substantial correlation between the LSM and DInSAR map. Furthermore, it was found that ~?88% of the very high and high landslide risk areas coincided with location of the past landslide events. These facts suggest that the algorithm and data sources used were sufficient to produce a sufficiently accurate LSM, which may be used for various purposes such as urban planning.     

Assessment of Flood Losses with Household Responses: Agent-Based Simulation in an Urban Catchment Area

Densely populated coastal urban areas are often exposed to multiple hazards, in particular floods and storms. Flood defenses and other engineering measures contribute to the mitigation of flood hazards, but a holistic approach to flood risk management should consider other interventions from the human side, including warning information, adaptive behavior, people/property evacuation, and the multilateral relief in local communities. There are few simulation approaches to consider these factors, and these typically focus on collective human actions. This paper presents an agent-based model that simulates flood response preferences and actions taken within individual households to reduce flood losses. The model implements a human response framework in which agents assess different flood scenarios according to warning information and decide whether and how much they invest in response measures to reduce potential inundation damages. A case study has been carried out in the Ng Tung River basin, an urbanized watershed in northern Hong Kong. Adopting a digital elevation model (DEM) as the modeling environment and a building map of household locations in the case area, the model considers the characteristics of households and the flood response behavior of their occupants. We found that property value, warning information, and storm conditions all influence household losses, with downstream and high density areas being particularly vulnerable. Results further indicate (i) that a flood warning system, which provides timely, accurate, and broad coverage rainstorm warning, can reduce flood losses by 30–40%; and (ii) to reduce losses, it is more effective and cheaper to invest early in response measures than late actions. This dynamic agent-based modeling approach is an innovative attempt to quantify and model the role of human responses in flood loss assessments. The model is demonstrated being useful for analyzing household scale flood losses and responses and it has the potential to contribute to flood emergency planning resource allocation in pluvial flood incidents.     

Temporal and spatial change detecting (1998–2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ images

Land use/land cover (LULC) has a profound impact on economy, society and environment, especially in rapid developing areas. Rapid and prompt monitoring and predicting of LULC’s change are crucial and significant. Currently, integration of Geographical Information System (GIS) and Remote Sensing (RS) methods is one of the most important methods for detecting LULC’s change, which includes image processing (such as geometrical-rectifying, supervised-classification, etc.), change detection (post-classification), GIS-based spatial analysis, Markov chain and a Cellular Automata (CA) models, etc. The core corridor of Pearl River Delta was selected for studying LULC’s change in this paper by using the above methods for the reason that the area contributed 78.31% (1998)–81.4% (2003) of Gross Domestic Product (GDP) to the whole Pearl River Delta (PRD). The temporal and spatial LULC’s changes from 1998 to 2003 were detected by RS data. At the same time, urban expansion levels in the next 5 and 10 years were predicted temporally and spatially by using Markov chain and a simple Cellular Automata model respectively. Finally, urban expansion and farmland loss were discussed against the background of China’s urban expansion and cropland loss during 1990–2000. The result showed: (1) the rate of urban expansion was up to 8.91% during 1998–2003 from 169,078.32 to 184,146.48 ha; (2) the rate of farmland loss was 5.94% from 312,069.06 to 293,539.95 ha; (3) a lot of farmland converted to urban or development area, and more forest and grass field converted to farmland accordingly; (4) the spatial predicting result of urban expansion showed that urban area was enlarged ulteriorly compared with the previous results, and the directions of expansion is along the existing urban area and transportation lines.     

Status of the Riparian Ecosystem in the Upper San Pedro River, Arizona: Application of an Assessment Model

A portion of Arizona’s San Pedro River is managed as a National Riparian Conservation Area but is potentially affected by ground-water withdrawals beyond the conservation area borders. We applied an assessment model to the Conservation Area as a basis for monitoring long-term changes in riparian ecosystem condition resulting from changes in river water availability, and collected multi-year data on a subset of the most sensitive bioindicators. The assessment model is based on nine vegetation bioindicators that are sensitive to changes in surface water or ground water. Site index scores allow for placement into one of three condition classes, each reflecting particular ranges for site hydrology and vegetation structure. We collected the bioindicator data at 26 sites distributed among 14 reaches that had similar stream flow hydrology (spatial flow intermittency) and geomorphology (channel sinuosity, flood-plain width). Overall, 39% of the riparian corridor fell within condition class 3 (the wettest condition), 55% in condition class 2, and 6% in the driest condition class. Condition class 3 reaches have high cover of herbaceous wetland plants (e.g., Juncus and Schoenoplectus spp.) along the perennial stream channel and dense, multi-aged Populus-Salix woodlands in the flood plain, sustained by shallow ground water in the stream alluvium. In condition class 2, intermittent stream flows result in low cover of streamside wetland herbs, but Populus-Salix remain abundant in the flood plain. Perennial wetland plants are absent from condition class 1, reflecting highly intermittent stream flows; the flood plain is vegetated by Tamarixa small tree that tolerates the deep and fluctuating ground water levels that typify this reach type. Abundance of herbaceous wetland plants and growth rate of Salix gooddingii varied between years with different stream flow rates, indicating utility of these measures for tracking short-term responses to hydrologic change. Repeat measurement of all bioindicators will indicate long-term trends in hydro-vegetational condition.     

Integrated groundwater resource management in Indus Basin using satellite gravimetry and physical modeling tools

Reliable and frequent information on groundwater behavior and dynamics is very important for effective groundwater resource management at appropriate spatial scales. This information is rarely available in developing countries and thus poses a challenge for groundwater managers. The in situ data and groundwater modeling tools are limited in their ability to cover large domains. Remote sensing technology can now be used to continuously collect information on hydrological cycle in a cost-effective way. This study evaluates the effectiveness of a remote sensing integrated physical modeling approach for groundwater management in Indus Basin. The Gravity Recovery and Climate Experiment Satellite (GRACE)-based gravity anomalies from 2003 to 2010 were processed to generate monthly groundwater storage changes using the Variable Infiltration Capacity (VIC) hydrologic model. The groundwater storage is the key parameter of interest for groundwater resource management. The spatial and temporal patterns in groundwater storage (GWS) are useful for devising the appropriate groundwater management strategies. GRACE-estimated GWS information with large-scale coverage is valuable for basin-scale monitoring and decision making. This frequently available information is found useful for the identification of groundwater recharge areas, groundwater storage depletion, and pinpointing of the areas where groundwater sustainability is at risk. The GWS anomalies were found to favorably agree with groundwater model simulations from Visual MODFLOW and in situ data. Mostly, a moderate to severe GWS depletion is observed causing a vulnerable situation to the sustainability of this groundwater resource. For the sustainable groundwater management, the region needs to implement groundwater policies and adopt water conservation techniques.     

Assessment of flood adaptive capacity of urban areas in Thailand

Urban floods have a significant impact on the economy, livelihoods, and daily activities. Despite extensive research on flood adaptation, there is a lack of consensus on ways to assess flood adaptive capacity. This research thus proposes an assessment framework of flood adaptive capacity of urban residents in flood-prone areas. The indicators are based on six components: economic resources, social capital, awareness and training, technology, infrastructure, and institutions and policies. The relevancy of the indicators was evaluated by experts in related fields, and a questionnaire was developed to collect data from a random sample of 230 households in 10 flood-prone municipalities in Thailand. Statistical analysis and structural equation modeling were subsequently performed to validate the components and indicators, and a flood-adaptability questionnaire was constructed. As a trial implementation, the questionnaire was applied to a target flood-prone municipality, Phetchaburi, to assess its adaptive capacity to flooding, expressed as flood adaptive capacity index. The adaptive capacity of Phetchaburi municipality to flooding was found to be high indicating the improvement in economic resources and infrastructure components. These findings reveal that the proposed framework is suitable for assessing the flood adaptive capacity of urban residents and useful for integrated flood risk assessment and management.     

Using NOAA AVHRR data to assess flood damage in China

The article used two NOAA-14 Advanced Very High ResolutionRadiometer (AVHRR) datasets to assess flood damage in the middleand lower reaches of China's Changjiang River (Yangtze River) in 1998. As the AVHRR is an optical sensor, it cannot penetratethe clouds that frequently cover the land during the flood season, and this technology is greatly limited in flood monitoring. However the widely used normalized difference vegetation index (NDVI) can be used to monitor flooding, sincewater has a much lower NDVI value than other surface features.Though many factors other than flooding (e.g. atmospheric conditions, different sun-target-satellite angles, and cloud) can change NDVI values, inundated areas can be distinguished fromother types of ground cover by changes in the NDVI value beforeand after the flood after eliminating the effects of other factors on NDVI. AVHRR data from 26 May and 22 August, 1998 wereselected to represent the ground conditions before and after flooding. After accurate geometric correction by collecting GCPs,and atmospheric and angular corrections by using the 6S code, NDVI values for both days and their differences were calculatedfor cloud-free pixels. The difference in the NDVI values betweenthese two times, together with the NDVI values and a land-use map, were used to identify inundated areas and to assess the arealost to the flood. The results show a total of 358 867 ha, with 207 556 ha of cultivated fields (paddy and non-irrigated field) inundated during the flood of 1998 in the middle and lower reaches of the Changjiang River Catchment; comparing with the reported total of 321 000 and 197 000 ha, respectively. The discrimination accuracy of this method was tested by comparing the results from two nearly simultaneous sets of remote-sensingdata (NOAA's AVHRR data from 10 September, 1998, and JERS-1 synthetic aperture radar (SAR) data from 11 September, 1998, with a lag of about 18.5 hr) over a representative flooded regionin the study area. The results showed that 67.26% of the total area identified as inundated using the NOAA data was also identified as inundated using the SAR data.     

基于异质人群响应特征的交通噪声暴露评估研究

考虑异质人群不同声功能需求和时空分布,对异质人群交通噪声暴露特征进行评估。通过集记人口高斯分解和噪声预测,获取特征人群分布数据和交通噪声数据;基于特征人群年龄和声功能需求,标定各年龄段人群噪声响应函数并进行归一化处理,构建异质人群噪声响应曲线;构建交通噪声暴露评估模型,结合获取数据及噪声响应曲线进行噪声暴露评估。结果表明,3类声功能区中人群噪声暴露与年龄变量均呈现类抛物线趋势,40岁左右人群暴露影响较儿童和老人低59.9%左右。人均噪声暴露在夜间明显偏高,尤其在声功能需求较高的第1类声功能区,其人均噪声超标值比昼间高7 d B。特征人群的空间分布对噪声暴露影响显著,工作时段学校区域适学人群集中,其总噪声暴露风险为同等状况住宅区的1.2倍。综合考虑人群特征和时空分布等因素,可更科学地进行区域交通噪声污染评估。     

2008-2016年臭氧监测试点城市的臭氧污染特征

选取臭氧试点城市北京、沈阳、上海和重庆,通过对2008-2016年臭氧监测数据进行分析研究,可以看出4个试点城市中北京的臭氧污染最严重。4个城市的臭氧污染特征均为高浓度臭氧所占比例较大,高值比较高,低浓度臭氧所占比例较小。北京、沈阳和上海的年平均臭氧浓度总体呈上升趋势。北京、上海、重庆、沈阳4个城市9年的超标天数比例分别为15.9%、7.7%、3.9%、6.5%。上海的臭氧浓度在秋季非常高。2012年的臭氧变化趋势比较异常,可能是由于2012年发生的不寻常气候条件导致。4个城市的臭氧浓度变化和气象条件的变化显著相关。     

Assessing land use impacts on flood processes in complex terrain by using GIS and modeling approach

A distributed hydrologic modeling and GIS approach is applied for the assessment of land use impact in the Steinsel sub-basin, Alzette, Grand-Duchy of Luxembourg. The assessment focuses on the runoff contributions from different land use classes and the potential impact of land use changes on runoff generation. The results show that the direct runoff from urban areas is dominant for a flood event compared with runoff from other land use areas in this catchment, and tends to increase for small floods and for the dry season floods, whereas the interflow from forested, pasture and agricultural field areas contributes to the recession flow. Significant variations in flood volume, peak discharge, time to the peak, etc., are found from the model simulation based on the three hypothetical land use change scenarios.     

Can groundwater vulnerability models assess seawater intrusion?

This study examines the uncertainty associated with two commonly used GIS-based groundwater vulnerability models, DRASTIC and EPIK, in assessing seawater intrusion, a growing threat along coastal urban areas due to overexploitation of groundwater resources. For this purpose, concentrations of Total Dissolved Solids (TDS) in groundwater samples at three pilot areas along the Eastern Mediterranean were compared with mapped vulnerability predictions obtained through DRASTIC and EPIK. While field measurements demonstrated high levels of groundwater salinity depending on the density of urbanization, both vulnerability assessment methods exhibited a limited ability in capturing saltwater intrusion dynamics. In the three pilot areas, DRASTIC was only able to predict correctly between 8.3 and 55.6% of the salinity-based water quality ranges, while EPIK's predictions ranged between 11.7 and 77.8%. This emphasizing that conventional vulnerability models perform poorly when anthropogenic impacts induce lateral flow processes such as seawater intrusion caused primarily by vertical groundwater extraction.     

A GIS-based approach for the long-term prediction of human health risks at contaminated sites

A Health Index/Risk Evaluation Tool (HIRET) has been developed for the integration of risk assessment and spatial planning using GIS capabilities. The method is meant to assist decision makers and site owners in the evaluation of potential human health risk with respect to land use. Human health risk defined as the potential adverse effects on human life or health is generally accepted as the most important aspect for site assessment and planning of remediation strategies. It concerns polluted sites that endanger human health on one hand and derelict land that does not cause the immediate risk on the other hand. In current state-of-the-art risk-assessment, long-term spatial and temporal changes of risks, in relation to changes in contamination patterns and land use functions, are not taken into account. The aim of this paper is to demonstrate the methodology developed for human health risk assessment in aspect of spatial and temporal domain. HIRET was developed as an extension for ESRI software ArcView 3.2 and allows performing dynamic human health risk assessment in long-term period, which is relevant for land use planning. The paper illustrates how such methodology can assist in environmental decision-making to enhance the efficiency of contaminated land management. A case study of contaminated site is given showing how data can be used within a GIS framework to produce maps indicating areas of potential human health risk.     

太湖流域景观生态风险评估

在GIS和RS技术支持下,以遥感数据和土地利用数据为基础,以行政区划为评价单元,构建景观生态风险评价模型,定量评价了研究区内景观生态风险的时空动态变化特征。结果表明,较低风险区主要分布于以苕溪为主的山地丘陵区,中等风险区主要分布于农业发达的平原区,较高风险区及高风险区主要集中于沿湖经济发达区。     

Uncertainty assessment of mapping mercury contaminated soils of a rapidly industrializing city in the Yangtze River Delta of China using sequential indicator co-simulation

Accurate characterization of heavy-metal contaminated areas and quantification of the uncertainties inherent in spatial prediction are crucial for risk assessment, soil remediation, and effective management recommendations. Topsoil samples (0–15 cm) (n = 547) were collected from the Zhangjiagang suburbs of China. The sequential indicator co-simulation (SIcS) method was applied for incorporating the soft data derived from soil organic matter (SOM) to simulate Hg concentrations, map Hg contaminated areas, and evaluate the associated uncertainties. High variability of Hg concentrations was observed in the study area. Total Hg concentrations varied from 0.004 to 1.510 mg kg⁻¹ and the coefficient of variation (CV) accounts for 70%. Distribution patterns of Hg were identified as higher Hg concentrations occurred mainly at the southern part of the study area and relatively lower concentrations were found in north. The Hg contaminated areas, identified using the Chinese Environmental Quality Standard for Soils critical values through SIcS, were limited and distributed in the south where the SOM concentration is high, soil pH is low, and paddy soils are the dominant soil types. The spatial correlations between Hg and SOM can be preserved by co-simulation and the realizations generated by SIcS represent the possible spatial patterns of Hg concentrations without a smoothing effect. Once the Hg concentration critical limit is given, SIcS can be used to map Hg contaminated areas and quantitatively assess the uncertainties inherent in the spatial prediction by setting a given critical probability and calculating the joint probability of the obtained areas.     

Environmental Factors and Risk Areas of West Nile Virus in Southern California, 2007–2009

The West Nile virus (WNV) may post a significant health risk for mammals, including humans and insects. This study examines the spatial–temporal effects of environmental factors on WNV dissemination with a case study of ten counties in the southern California, where the epidemic was recently most prevalent within the USA. WNV surveillance data were obtained from the California Vectorborne Disease Surveillance System and Centers for Disease Control and Prevention. Remote sensing and Geographic Information Systems (GIS) techniques were combined to derive environmental variables. Principal component analysis was performed to select the most relevant environmental variables. Two ecological zones were identified based on the selected variables. Identification of risk areas for WNV was limited to a zone with 95% mosquitoes surveillance records. Three time windows, the epidemiological weeks?18–26, 27–35, and 36–44 in each year of 2007–2009, were examined in details with risk area mapping. It is found that the southern part of San Joaquin Valley in Kern County and Los Angeles County (especially its southern part) were the most vulnerable locations for WNV outbreak. Main factors contributing to the WNV propagation included summer mean temperature, annual mean deviation from the mean temperature, land surface temperature, elevation, landscape complexity, landscape diversity, and vegetation water content. The result of this study improves understanding of WNV ecology and provides tools for detecting, tracking, and predicting the epidemic. The holistic approach developed for this study, which integrated remotely sensed, GIS-based, and in situ-measured environmental data with landscape metrics, may be applied to studies of other vector-borne diseases.     

Distributed process modeling for regional assessment of coastal vulnerability to sea-level rise

Sea-level rise involves increases in the coastal processes of inundation and erosion which are affected by a complex interplay of physical environmental parameters at the coast. Many assessments of coastal vulnerability to sea-level rise have been detailed and localised in extent. There is a need for regional assessment techniques which identify areas vulnerable to sea-level rise. Four physical environmental parameters – elevation, exposure, aspect and slope, are modeled on a regional scale for the Northern Spencer Gulf (NSG) study area using commonly available low-resolution elevation data of 10 m contour interval and GIS-based spatial modeling techniques. For comparison, the same parameters are modeled on a fine-scale for the False Bay area within the NSG using high-resolution elevation data. Physical environmental parameters on the two scales are statistically compared to coastal vulnerability classes as identified by Harvey et al. [1] using the Spearman rank-correlation test and stepwise linear regression. Coastal vulnerability is strongly correlated with elevation and exposure at both scales and this relationship is only slightly stronger for the high resolution False Bay data. The results of this study suggest that regional scale distributed coastal process modeling may be suitable as a first cut in assessing coastal vulnerability to sea-level rise in tide-dominated, sedimentary coastal regions. Distributed coastal process modeling provides a suitable basis for the assessment of coastal vulnerability to sea-level rise of sufficient accuracy for on-ground management and priority-setting on a regional scale.     

基于遥感数据的京津冀地区PM2.5时空分布特征

基于MODIS AOD遥感数据,采用多元线性回归模型对PM2.5地面监测数据进行模拟估算,同时加入降水量、相对湿度等气象因子以提高模型精度,结合GIS空间分析技术,得到2015—2016年京津冀地区空间连续的PM2.5浓度分布。结果表明:利用多元线性回归模型反演PM2.5浓度效果较好,R 2均在0.59~0.84之间。在时间上,京津冀地区PM2.5浓度呈现出夏季最低、秋季稍高、冬春两季最高的变化趋势;在空间上,2015年和2016年京津冀地区PM2.5浓度有明显的区域差异,均呈现出西北低、东南高的分布格局,大致与燕山山脉和太行山脉走向一致。     

Assessment of groundwater vulnerability in the Yinchuan Plain,Northwest China using OREADIC

Groundwater vulnerability assessments provide a measure of the sensitivity of groundwater quality to an imposed contaminant load and are globally recognized as an essential element of all aquifer management and protection plans. In this paper, the vulnerability of groundwaters underlying the Yinchuan Plain of Northwest China is determined using OREADIC, a GIS-based assessment tool that incorporates the key characteristics of the universally popular DRASTIC approach to vulnerability assessment but has been modified to consider important additional hydrogeological factors that are specific to the region. The results show that areas of high vulnerability are distributed mainly around Qingtongxia City, Wuzhong City, Lingwu City, and Yongning County and are associated with high rates of aquifer recharge, shallow depths to the water table, and highly permeable aquifer materials. The presence of elevated NO₃⁻ in the high vulnerability areas endorses the OREADIC approach. The vulnerability maps developed in this study have become valuable tools for environmental planning in the region and will be used for predictive management of the groundwater resource.     

Comprehensive analysis of an ecological risk assessment of the Daliao River estuary, China

At present, most estuarine ecological risk studies are based on terrestrial ecosystem models, which ignore spatial heterogeneity. The Daliao River estuary has representative characteristics of many estuaries in China, and we used this estuary as the study area to formulate an estuarine ecological risk evaluation model. Targeting the estuary's special hydrodynamic condition, this model incorporated variables that were under the influence of human activities and used them as the major factors for partitioning sections of the river according to risk values. It also explored the spatial and temporal distribution laws of estuarine ecological risk. The results showed that, on the whole, the ecological risk of the Daliao River estuary area was relatively high. At a temporal level, runoff was the main factor resulting in differences in ecological risk, while at the spatial level, the ecological risk index was affected by pollutants carried by runoff from upstream, as well as downstream pollution emissions and dilution by seawater at the mouth of the sea. The characteristics of this model make it possible to simulate the spatial and temporal risk distribution in different regions and under different rainfall regimes. This model can thus be applied in other estuarine areas and provides some technical support for analysis and control of ecological destruction in estuary areas.