城市热岛效应和气溶胶浓度的动力、热力学分析

在能量平衡方程中引入气溶胶的吸收和散射作用,并与三维行星边界层运动方程组相耦合,根据温度分布显式求解运动场,探讨三维行星边界层内温度、运动、气溶胶浓度分布特征.结果表明,城市人为热释放直接决定了城市热岛效应的强度,城市面积越大,城市热岛效应的强度也越强,城市面积固定时,城市越分散,城市热岛效应的强度越弱,这为城市建设多采取卫星城的方式提供了一定的理论支撑.气溶胶的散射作用要大于吸收作用,其对城市热岛效应的强度主要起削弱作用,当气溶胶浓度较大时,吸收作用更显著一些,此时城市热岛效应的强度会有一定的增强,但是幅度不大.当城市热岛效应的强度增强时,其所驱动的环流也会增强,造成城区中心气溶胶浓度略有下降.     

An integrated fuzzy-stochastic modeling approach for assessing health-impact risk from air pollution

High concentrations of air pollutants in the ambient environment can result in breathing problems with human communities. Effective assessment of health-impact risk from air pollution is important for supporting decisions of the related detection, prevention, and correction efforts. However, the quality of information available for environmental/health risk assessment is often not satisfactory enough to be presented as deterministic numbers. Stochastic method is one of the methods for tackling those uncertainties, by which uncertain information can be presented as probability distributions. However, if the uncertainties can not be presented as probabilities, they can then be handled through fuzzy membership functions. In this study, an integrated fuzzy-stochastic modeling (IFSM) approach is developed for assessing air pollution impacts towards asthma susceptibility. This development is based on Monte Carlo simulation for the fate of SO₂ in the ambient environment, examination of SO₂ concentrations based on the simulation results, quantification of evaluation criteria using fuzzy membership functions, and risk assessment based on the combined fuzzy-stochastic information. The IFSM entails (a) simulation for the fate of pollutants in ambient environment, with the consideration of source/medium uncertainties, (b) formulation of fuzzy air quality management criteria under uncertain human-exposure pathways, exposure dynamics, and SPG-response variations, and (c) integrated risk assessment under complexities of the combined fuzzy/stochastic inputs of contamination level and health effect (i.e., asthma susceptibility). The developed IFSM is applied to a study of regional air quality management. Reasonable results have been generated, which are useful for evaluating health risks from air pollution. They also provide support for regional environmental management and urban planning.     

Singular value decomposition analysis of spatial relationships between monthly weather and air pollution index in China

Spatial patterns are important features for understanding regional air quality variability. Statistical analysis tools, such as empirical orthogonal function (EOF), have been extensively used to identify and classify spatial patterns. These tools, however, do not directly reveal the related weather conditions. This study used singular value decomposition (SVD) to identify spatial air pollution index (API) patterns related to meteorological conditions in China, one of world’s regions facing catastrophic air pollution. The monthly API and four meteorological variables (precipitation, surface air temperature, humidity, and wind speed) during 2001–2012 in 42 cities in China were used. The two leading SVD spatial patterns display the API anomalies with the same sign across China and opposite signs between northern and southern China, respectively. The meteorological variables have different relationships with these patterns. For the first pattern, wind speed is the most important. The key regions, where the correlations between the API field and the wind speed’s SVD time series are significant at the 99% confidence level, are found nationwide. Precipitation and air temperature are also important in the southern and northern portions of eastern China, respectively. For the second pattern, the key regions occur mainly in northern China for temperature and humidity and southern China for wind speed. Air humidity has the largest contribution to this pattern. The weather-API relationships characterized by these spatial patterns are useful for selecting factors for statistical air quality prediction models and determining the geographic regions with high prediction skills.     

Geographies of uncertainty in the health benefits of air quality improvements

Assessing the long-term benefits of marginal improvements in air quality from regulatory intervention is methodologically challenging. In this study, we explore how the relative risks (RRs) of mortality from air pollution exposure change over time and whether patterns in the RRs can be attributed to air quality improvements. We employed two-stage multilevel Cox models to describe the association between air pollution and mortality for 51 cities with data from the American Cancer Society (ACS) cohort (N = 264,299, deaths = 69,819). New pollution data were computed through models that predict yearly average fine particle (PM_2.5) concentrations throughout the follow-up (1982–2000). Average PM_2.5 concentrations from 1999 to 2000 and sulfate concentrations from 1980 were also examined. We estimated the RRs of mortality associated with air pollution separately for five time periods (1982–1986, 1987–1990, 1991–1994, 1995–1998, and 1999–2000). Mobility models were implemented with a sub-sample of 100,557 subjects to assist with interpreting the RR estimates. Sulfate RRs exhibit a large decline from the 1980s to the 1990s. In contrast, PM_2.5 RRs follow the opposite pattern, with larger RRs later in the 1990s. The reduction in sulfate RR may have resulted from air quality improvements that occurred through the 1980s and 1990s in response to the acid rain control program. PM_2.5 concentrations also declined in many places, but toxic mobile sources are now the largest contributors to PM in urban areas. This may account for the heightened RR of mortality associated with PM_2.5 in the 1990s. The paper concludes with a three alternative explanations for the temporal pattern of RRs, each emphasizing the uncertainty in ascribing health benefits to air quality improvements.     

Study of statistically correcting model CMAQ-MOS for forecasting regional air quality

Based on analysis of the air pollution observational data at 8 observation sites in Beijing including outer suburbs during the period from September 2004 to March 2005, this paper reveals synchronal and in-phase characteristics in the spatial and temporal variation of air pollutants on a city-proper scale at deferent sites; describes seasonal differences of the pollutant emission influence between the heating and non-heating periods, also significantly local differences of the pollutant emission influence between the urban district and outer suburbs, i.e. the spatial and temporal distribution of air pollutant is closely related with that of the pollutant emission intensity. This study shows that due to complexity of the spatial and temporal distribution of pollution emission sources, the new generation Community Multi-scale Air Quality (CMAQ) model developed by the EPA of USA produced forecasts, as other models did, with a systematic error of significantly lower than observations, albeit the model has better capability than previous models had in predicting the spatial distribution and variation tendency of multi-sort pollutants. The reason might be that the CMAQ adopts average amount of pollutant emission inventory, so that the model is difficult to objectively and finely describe the distribution and variation of pollution emission sources intensity on different spatial and temporal scales in the areas, in which the pollution is to be forecast. In order to correct the systematic prediction error resulting from the average pollutant emission inventory in CMAQ, this study proposes a new way of combining dynamics and statistics and establishes a statistically correcting model CMAQ-MOS for forecasts of regional air quality by utilizing the relationship of CMAQ outputs with corresponding observations, and tests the forecast capability. The investigation of experiments presents that CMAQ-MOS reduces the systematic errors of CMAQ because of the uncertainty of pollution emission inventory and improves the forecast level of air quality. Also this work employed a way of combining point and area forecasting, i.e. taking the products of CMAQ for a center site to forecast air pollution for other sites in vicinity with the scheme of model products "reanalysis" and average over the "area".     

Bayesian modelling of environmental risk: example using a small area ecological study of coronary heart disease mortality in relation to modelled outdoor nitrogen oxide levels

Bayesian modelling of health risks in relation to environmental exposures offers advantages over conventional (non-Bayesian) modelling approaches. We report an example using research into whether, after controlling for different confounders, air pollution (NO_x) has a significant effect on coronary heart disease mortality, estimating the relative risk associated with different levels of exposure. We use small area data from Sheffield, England and describe how the data were assembled. We compare the results obtained using a generalized (Poisson) log-linear model with adjustment for overdispersion, with the results obtained using a hierarchical (Poisson) log-linear model with spatial random effects. Both classes of models were fitted using a Bayesian approach. Including spatial random effects models both overdispersion and spatial autocorrelation effects arising as a result of analysing data from small contiguous areas. The first modelling framework has been widely used, while the second provides a more rigorous model for hypothesis testing and risk estimation when data refer to small areas. When the models are fitted controlling only for the age and sex of the populations, the generalized log-linear model shows NO_x effects are significant at all levels, whereas the hierarchical log-linear model with spatial random effects shows significant effects only at higher levels. We then adjust for deprivation and smoking prevalence. Uncertainty in the estimates of smoking prevalence, arising because the data are based on samples, was accounted for through errors-in-variables modelling. NO_x effects apparently are significant at the two highest levels according to both modelling frameworks.

Analysis of the influence of parameter and scale uncertainties on a local multi-criteria land use evaluation model

Land use evaluation involves careful consideration of several environmental factors and their relative importance quantified by factor weights. Local multi-criteria evaluation provides a mechanism for computing factor (criteria) weights within local neighborhoods that capture spatial heterogeneity and contribute to more accurate evaluation results. The accuracy of results, however, is tempered by the potential uncertainty of criteria weights. The paper presents a spatially explicit approach to uncertainty and sensitivity analysis of local criteria weights and modeling scale on the variability of model output. The efficacy of the approach is presented on the example of Environmental Benefit Index (EBI) model used by the U.S. Department of Agriculture Conservation Reserve Program (CRP) to select environmentally sensitive agricultural areas for conservation. The uncertainty analysis resulted in identifying robust areas for CRP selection characterized by high suitability and low uncertainty. The sensitivity analysis focused on the next-best group of candidates characterized by high suitability and high uncertainty. The results show that there is a relationship between spatial heterogeneity, data representation scale, and the level of uncertainty in the results of EBI model. The sensitivity of model output can be attributed to both the uncertainty of criteria weights and the modeling scale. A potential practical value of this approach is the improved analytical support for land suitability evaluation requiring a consideration of sub-optimal land units (high suitability/high uncertainty). Also, this approach can guide modelling effort by allowing the analyst to visualize spatial distribution and patterns of model output uncertainty and focus data collection on influential model input factors.     

Risks of airborne pollution accidents in a major conurbation: case study of Zhangjiakou,a host city for the 2022 Winter Olympics

The number of airborne pollution accidents is second only to that of water-borne pollution accidents, in recorded environmental disasters. Acute casualties and public health costs have prompted many airborne pollution risk analyses. To date, few assessment methods have been carried out at regional-scale to quantify acute airborne pollution risk. Herein, a Hybrid Simulation and Risk Analysis approach, involving a systematic combination of simulation, risk ranking, and standardized analysis, is proposed at regional scale. Gaussian and heavy-gas models are utilized in the simulation process, and acute exposure limits preferentially adopted in the risk analysis. The case study shows that 34 of 243 townships in Zhangjiakou City of north China, one of the twin cities selected to host the 2022 Winter Olympics, are threatened by airborne risk sources. It is found that the accidental air pollution risk is comparatively higher in the Xuanhua and Wanquan conurbations. High-risk chemical enterprises (312–432 risk scores) are mostly located near urban areas with high population density where many people are vulnerable receptors to potential air pollution accidents. The resulting risk map indicates that acute airborne pollution from Zhangjiakou would not be a threat either to the proposed Olympic site at Chongli or to downwind Beijing.     

Reduction of Nitrate Loadings to Ground Water

Non-point source pollution of ground water systems has become a national concern in recent years. Researchers and regulatory agencies are investigating the source and processes of the contamination. Agricultural best management practices (BMPs) traditionally developed to reduce non-point source pollution of surface water resources are being investigated for their impact on ground water quality. This study used the CREAMS model to simulate the long-term effects of seven different BMPs on nitrate nitrogen (NO₃-N) loadings to a shallow, unconfined ground water system. Two representative watersheds, 5.8 and 8.9 hectares (14.3 and 22 acres) in area, in the Coastal Plain physiographic region of Maryland were selected for study. Soils in these watersheds belong to the Matapeake silt loam series and have moderate infiltration capacity. Results from this study indicated that BMPs used in conjunction with winter cover (barley) reduced NO₃-N leaching to the ground water system. It was also found that turfgrass reduced surface losses of water and nitrogen, but increased leaching losses of water and NO₃-N significantly. All of the BMPs simulated in this study resulted in leachate NO₃-N concentrations exceeding 10 ppm, the U.S. EPA health standard for public drinking water, indicating a need for alternate practices for reducing nitrate leaching.     

Simulation of Air Quality using an ISCST3 Dispersion Model

Urban air quality is an issue of major concern across many cities in India. In particular, high levels of particulate matter (both SPM and RSPM) are responsible for noncompliance to air quality standards. Air quality modeling is an effective tool to simulate the air quality of a region and to predict air quality concentrations under different scenarios. Kanpur city which is a top‐ten urban conglomerate in India (based on population) is chosen for the application of the ISCST3 model and simulation of air quality. Sectored emission loads are estimated for transport, industrial, power, and domestic sectors, which provide an estimate of the major contributors to air pollution with specific reference to particulate matter, which is a major pollutant of concern. A detailed scenario analysis is carried out to estimate the changes in emissions that would take place due to various interventions. Dispersion modeling is carried out using the ISCST3 model, to estimate the concentrations of SPM all over the city under different scenarios. Emission inventory and meteorological data served as input to the model, and the air quality is predicted for various seasons and intervention scenarios. The modeled values for the scenario without intervention results in an underestimation of 48%, which is due to unaccountable or unidentified sources, trans‐boundary movement of SPM, and model calibration errors. To overcome the error, the model is calibrated with the observed values and results are obtained for other scenarios using the calibration factor. The paper demonstrates only the research direction currently used to simulate air quality in Indian cities. However, further refinement and research is required before it could be used for more accurate predictions.     

Spectral band decomposition combined with nonlinear models: application to indoor formaldehyde concentration forecasting

This paper proposes a new approach for forecasting continuous indoor air quality time series and in particular the concentration of a common air pollutant in offices like formaldehyde. Forecasting is achieved through the combination of the spectral band decomposition using fast Fourier transform and nonlinear time series modeling. Two nonlinear models have been tested: a threshold autoregressive (TAR) model and a Chaos dynamics-based modeling. This study shows the benefit of the Fourier decomposition coupled with nonlinear modeling of each extracted component, compared to forecasting applied directly on the raw data. Both TAR and Chaos dynamics models are able to reproduce nonlinearities, with slightly better performance in the case of the second model. These hybrid models provide good performance on forecast time horizon up to 12 h ahead.     

Multi-city sustainable regional urban growth simulation—MSRUGS: a case study along the mid-section of Silk Road of China

Urban growth along the middle section of the ancient silk-road of China (so called West Yellow River Corridor—He-Xi Corridor) has taken a unique path deviating from what is commonly seen in the coastal China. Urban growth here has been driven by historical heritage, transportation connection between East and West China, and mineral exploitation. However, it has been constrained by water shortage and harsh natural environment because this region is located in arid and semi-arid climate zones. This paper attempts to construct a multi-city agent-based model to explore possible trajectories of regional urban growth along the entire He-Xi Corridor under a severe environment risk, over urban growth under an extreme threat of water shortage. In contrast with current ABM approaches, our model will simulate urban growth in a large administrative region consisting of a system of cities. It simultaneously considers the spatial variations of these cities in terms of population size, development history, water resource endowment and sustainable development potential. It also explores potential impacts of exogenous inter-city interactions on future urban growth on the basis of urban gravity model. The algorithmic foundations of three types of agents, developers, conservationists and regional-planners, are discussed. Simulations with regard to three different development scenarios are presented and analyzed.     

Linking MODFLOW with an Agent-Based Land-Use Model to Support Decision Making

The U.S. Geological Survey numerical groundwater flow model, MODFLOW, was integrated with an agent-based land-use model to yield a simulator for environmental planning studies. Ultimately, this integrated simulator will be used as a means to organize information, illustrate potential system responses, and facilitate communication within a participatory modeling framework. Initial results show the potential system response to different zoning policy scenarios in terms of the spatial patterns of development, which is referred to as urban form, and consequent impacts on groundwater levels. These results illustrate how the integrated simulator is capable of representing the complexity of the system. From a groundwater modeling perspective, the most important aspect of the integration is that the simulator generates stresses on the groundwater system within the simulation in contrast to the traditional approach that requires the user to specify the stresses through time.     

Spatial heterogeneity of economic development and industrial pollution in urban China

The relationship between economic development and environmental pollution has been widely studied in the context of the environmental Kuznets curve. This study applies the three-dimension framework of density, division, and distance proposed by the World Bank to identify the spatial heterogeneity of development and pollution in urban China. An inverted U relationship is detected between density and industrial SO₂ emission, while a cubic relationship is found between density and industrial SO₂/soot emission intensity. The statistical significance of division indicates that the pollution haven hypothesis holds in the western region and cities in the periphery. The environmental implication of distance is that the industrial pollution is largely concentrated in the national and regional cores.     

Forecasting of Air Quality Index in Delhi Using Neural Network Based on Principal Component Analysis

Forecasting of the air quality index (AQI) is one of the topics of air quality research today as it is useful to assess the effects of air pollutants on human health in urban areas. It has been learned in the last decade that airborne pollution has been a serious and will be a major problem in Delhi in the next few years. The air quality index is a number, based on the comprehensive effect of concentrations of major air pollutants, used by Government agencies to characterize the quality of the air at different locations, which is also used for local and regional air quality management in many metro cities of the world. Thus, the main objective of the present study is to forecast the daily AQI through a neural network based on principal component analysis (PCA). The AQI of criteria air pollutants has been forecasted using the previous day’s AQI and meteorological variables, which have been found to be nearly same for weekends and weekdays. The principal components of a neural network based on PCA (PCA-neural network) have been computed using a correlation matrix of input data. The evaluation of the PCA-neural network model has been made by comparing its results with the results of the neural network and observed values during 2000–2006 in four different seasons through statistical parameters, which reveal that the PCA-neural network is performing better than the neural network in all of the four seasons.     

Numerical modelling of pollutant dispersion in sea breeze conditions

The characteristics of air pollution in Tarragona (Spain) were investigated. Tarragona has an important petrochemical industry in a coastal region with a complex terrain. The numerical study was made in sea breeze conditions with a three-dimensional mesoscale model. Temporal and spatial variations of the wind fields have been used in the Eulerian equation for a non-reactive pollutant. The results of this study reveal the complexity of the dispersion patterns due to the combined effects of the sea breeze circulation and the orography. This work presents a comparison between the model output and the observed wind data by sodar and surface wind measurements. The evaluation shows that the model is capable of providing very realistic wind fields within this domain.     

Associations between outdoor air quality and birth weight: a geostatistical sequential simulation approach in Coastal Alentejo,Portugal

Several environmental health studies suggest birth weight is associated with outdoor air pollution during gestation. In these studies, exposure assignments are usually based on measurements collected at air quality monitoring stations that do not coincide with health data locations. So, estimated exposures can be misleading if they do not take into account the uncertainty of exposure estimates. In this article we conducted a semi-ecological study to analyze associations between air quality during gestation and birth weight. Air quality during gestation was measured using a biomonitor, as an alternative to traditional air quality monitoring stations data, in order to increase spatial resolution of exposure measurements. To our knowledge this is the first time that the association between air quality and birth weight is studied using biomonitors. To address exposure uncertainty at health locations, we applied geostatistical simulation on biomonitoring data that provided multiple equally probable realizations of biomonitoring data, with reproduction of observed histogram and spatial covariance while matching for conditioning data. Each simulation represented a measure of exposure at each location. The set of simulations provided a measure of exposure uncertainty at each location. To incorporate uncertainty in our analysis we used generalized linear models, fitted simulation outputs and health data on birth weights and assessed statistical significance of exposure parameter using non-parametric bootstrap techniques. We found a positive association between air quality and birth weight. However, this association was not statistically significant. We also found a modest but significant association between air quality and birth weight among babies exposed to gestational tobacco smoke.     

Risk assessment of extreme air pollution based on partial duration series: IDF approach

The occurrences of extreme pollution events have serious effects on human health, environmental ecosystems, and the national economy. To gain a better understanding of this issue, risk assessments on the behavior of these events must be effectively designed to anticipate the likelihood of their occurrence. In this study, we propose using the intensity–duration–frequency (IDF) technique to describe the relationship of pollution intensity (i) to its duration (d) and return period (T). As a case study, we used data from the city of Klang, Malaysia. The construction of IDF curves involves a process of determining a partial duration series of an extreme pollution event. Based on PDS data, a generalized Pareto distribution (GPD) is used to represent its probabilistic behaviors. The estimated return period and IDF curves for pollution intensities corresponding to various return periods are determined based on the fitted GPD model. The results reveal that pollution intensities in Klang tend to increase with increases in the length of time between return periods. Although the IDF curves show different magnitudes for different return periods, all the curves show similar increasing trends. In fact, longer return periods are associated with higher estimates of pollution intensity. Based on the study results, we can conclude that the IDF approach provides a good basis for decision-makers to evaluate the expected risk of future extreme pollution events.     

Monthly spatial rainfall correlation functions and interpretations for Turkey

Abstract

Monthly spatial rainfall distribution features and their effects on spatial correlation patterns are significant in any regional study. In this paper, first a number of statistical terms and properties are explained with reference to the spatial correlation functions (SCFs). This is followed by the analysis of a theoretical spatial correlation model and its parameter estimation. Monthly empirical SCFs are examined in relation to spatial rainfall characteristics. In order to obtain a definite pattern, the SCF values are averaged in successive equal-distance groups. This average spatial correlation function shows a decreasing pattern with distance. Some interpretations of these spatial correlation functions are given for Turkey with discussion of the results obtained.     

Distribution and Potential Sources of Surface Aerosol Particles in the Yangtze River Delta Region,China

Based on meteorological and pollution data from January 2017 to December 2019, in this paper the long-term distribution of surface aerosol particles, and the interaction between aerosol pollution and meteorological factors in four cities of the Yangtze River Delta (YRD) region is investigated. The long-term observation shows the law of typical aerosol pollution characteristics. Meteorological factors are significantly different during aerosol-polluted and nonpolluted days. The effect of each meteorological factor on aerosol pollution may vary by different seasons and cities. The changes in meteorological factors are not completely consistent during aerosol fine-mode and coarse-mode polluted days. To distinguish the possible sources of surface aerosol particles, the potential source contribution function and concentration-weighted trajectory models are applied to study transport sources. Based on the detailed analyses, this study will provide a reliable basis for further pollution control in the YRD.