Grey Box and Component Models to Forecast Ozone Episodes: A Comparison Study

For the purpose of short-term forecasting of high ozone concentration episodes stochastic models have been suggested and developed in the literature. The present paper compares the quality of forecasts produced by a grey box and a component time-series model. The summer ozone patterns for three European urban areas (two continental and one mediterranean) are processed. By means of forecast performance indices according to EC and WHO guidelines, the following features of the models could be found: The grey box model is highly adaptive and produces forecasts with low error variance that increases with the time horizon of forecast. The component model is more 'stiff' that results in a higher forecast-error variance and poorer adaption in detail. The forecast horizon, however, could be enlarged with this model. The accuracy of predicting threshold exceedance is similar for both models. This can be understood from the assumption of a cyclical time development of ozone that was made for both models.     

Simulation of mixing depths incorporating the urban heat island effect

The main objective of this paper was to establish the effect of anthropogenic heat flux on the formation of mixing depth specially in urban areas. In this study an energy balance mixing depth model was developed for the simulation of mixing depth incorporating the heat island effect. To demonstrate the effect of heat island, mixing depths were simulated under two conditions. Under the first condition, hourly gridded anthropogenic heat flux and variant surface characteristics were considered, while under the second condition anthropogenic heat flux term was ignored and constant surface characteristics were considered throughout the modelling domain. The release of anthropogenic heat flux in the urban environment was found to be responsible for the growth of the unstable layer in the lower atmosphere even during the night. This resulted in higher mixing depth values in urban areas compared to the surrounding countryside. The maximum differential surfaces sensible heat flux between urban and rural areas was estimated as 130 W/m². From the simulation of mixing depth, it was revealed that the maximum increment in mixing depth in urban areas was 300 m due to the heat island effect. This increment in mixing depth in urban areas can provide an extra space for dilution and mixing of pollutants.     

A Wavelet-based Neural Network Model to Predict Ambient Air Pollutants’ Concentration

The present paper proposes a wavelet based recurrent neural network model to forecast one step ahead hourly, daily mean and daily maximum concentrations of ambient CO, NO₂, NO, O₃, SO₂ and PM_2.5 — the most prevalent air pollutants in urban atmosphere. The time series of each air pollutant has been decomposed into different time-scale components using maximum overlap wavelet transform (MODWT). These time-scale components were made to pass through Elman network. The number of nodes in the network was decided on the basis of the strength (power) of the corresponding input signals. The wavelet network model was then used to obtain one-step ahead forecasts for a period extending from January 2009 to June 2010. The model results for out of sample forecast are reasonably good in terms of model performance parameters such as mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized mean absolute error (NMSE), index of agreement (IOA) and standard average error (SAE). The MAPE values for daily maximum concentrations of CO, NO₂, NO, O₃, SO₂ and PM2.5 were found to be 9.5%, 17.37%, 21.20%, 13.79%, 17.77% and 11.94%, respectively, at ITO, Delhi, India. Bearing in mind that the forecasts are for daily maximum concentrations tested over a long validation period, the forecast performance of the model may be considered as reasonably good. The model results demonstrate that a judicious selection of wavelet network design may be employed successfully for air quality forecasting.     

An energy and mortality impact assessment of the urban heat island in the US

Increased summer energy use and increased summer heat related mortality are the two most cited detrimental impacts of the urban heat island (UHI). An assessment of these impacts was made that considered the annual impact of the UHI, not just the summer impact. It was found that in north of the US there was a net decrease in energy use from the UHI, as heating energy reductions were larger than the increase in cooling energy. In the south there was a net energy increase from the UHI. The impact of the UHI on heat related deaths was an estimated increase of 1.1 deaths per million people. The impact of the UHI on cold related deaths was an estimated decrease of 4.0 deaths per million people. These estimates are caveated by the acknowledgement that compounding factors influence mortality. Hypothermia related death rates were three times higher in rural areas than urban areas. This is surprising as the homeless population is usually considered the most at risk, yet they mostly live in urban areas.     

Simulation of Urban-Scale Air Pollution Patterns in Luxembourg: Contributing Sources and Emission Scenarios

The aim of this study is to investigate the air pollution situation in an urban area in southwestern Luxembourg and to simulate annual NO₂ and PM₁₀ concentrations in response to changes in meteorological conditions and emissions using a Gaussian dispersion model. Simulations are carried out for the years 1998–2006. Emission scenarios related to road transport and nonindustrial combustion are performed in order to predict changes of air pollution levels. Road transport is by far the most important local emission source in the study area. Scenarios with more stringent emission standards for vehicles, less traffic, and fewer heavy-duty vehicles lead to reductions of NO_x and primary PM₁₀ emissions. As a result, the annual NO₂ concentrations are decreasing in most parts of the study area and are below the European annual limit value of 40 μg?m^?3. In contrast, a scenario with increased use of wood pellets for domestic heating shows an increase in urban PM₁₀ concentration. The year-to-year variability of meteorological conditions accounts for the same magnitude of absolute NO₂ and PM₁₀ concentration changes as the emission scenarios. The comparison with measurements located in the study area shows that the model is able to predict urban-scale annual average air pollution. The proposed application results show that the model can be appropriate for policy-driven air quality management and planning queries.     

Characterization of PM10-bound polycyclic aromatic hydrocarbons in the ambient air of Spanish urban and rural areas

Urban areas constitute major pollution sources due to anthropogenic activities located in these areas. Among the legislated air pollutants, the particulate matter with an aerodynamic diameter less than or equal to 10 microns (PM10) and polycyclic aromatic hydrocarbons (PAH) are controlled under Directive 2008/50/EC and Directive 2004/107/EC, respectively due to their adverse health effects. A study was carried out at four urban and rural Spanish areas during the warm and cold seasons in 2008-2009 to quantify 19 PAH associated with the atmospheric PM10 by gas chromatography-mass spectrometry-mass spectrometry detection (GC-MS-MS) with the internal standard method. The particle-bound composition of the analysed PAH was 5 and 10 times greater in industrial and urban areas, respectively when compared to those measured in rural areas. The highest PAH concentrations during the cold period were possibly due to the additional contribution of domestic heating sources and meteorological conditions such as low temperature and solar irradiation. The use of molecular diagnostic ratios indicated that the possible, major PAH pollution sources in the most polluted areas were pyrogenic sources, mainly attributed to petroleum combustion sources (motor vehicle emissions and crude oil combustion). Petrogenic sources related to evaporative emissions also seemed to contribute in the most polluted area during the warm period. Those dates with high carcinogenic character according to the benzo(a)pyrene equivalent (BaP-eq) were also possibly attributed to petroleum combustion sources.     

Simulation of Wind Circulation and Pollutant Diffusion Over the Pearl River Delta Region

Changes in urban surface areas and population growth have significantly affected the weather and environment. Emissions of nitrogen oxides are increasing in the Pearl River Delta region. Nitrogen compounds emitted by factories and motor vehicles are the major sources of nitric pollution. To study the impacts of urbanization and the relationship between pollutant diffusion and the atmospheric environment, the nonhydrostatic mesoscale forecast model MM5 (v3.73), which was developed by Penn State University and the National Center of Atmospheric Research, and a mass continuity equation for air pollutants, were used in this study. Two experiments were designed. One experiment (BE) applied horizontal grid resolutions of 27, 9, 3, and 1?km in four nested domains. The other experiment adopted new land-use data (in domain 4) directly retrieved from Landsat Thematic Mapper imagery to replace the 1980s data of the United States Geological Survey in BE. A 48-h simulation (from 0000?UTC on 21 October to 0000?UTC on 23 October 2008) was conducted, with the first 12?h being the spin-up time and the remaining 36?h being the effective simulation, so as to capture the diurnal features of the thermally induced winds associated with the land–sea breeze and urban heat island circulations. The different results obtained from the two tests for wind circulation and air pollution dispersion and transportation in the Pearl River Delta region were analyzed. The simulated results show that the both experiments can well simulate land–sea breeze circulation and remarkable land–sea breeze evolution, comparing with observation data. The height of the PBL had a significant diurnal cycle. The structure of the wind field can obviously impact the dispersion of the NO _x in three dimensions. Nitrogen oxides mainly diffused along the dominant wind direction (east or southeast wind), therefore the majority of the pollutants accumulated in the northwest region of the fine domain in both simulation experiments. However, it induced the pollutants concentration in an irregular pattern due to the fine-resolution grid spaces and complicated inland wind field in the northwest area of the inner domain. Moreover, increasing the proportion of urban surface caused sensible heat flux increase, latent heat flux decrease and humility reducing relatively in the region of urban surface characteristics apparently. Urbanization will cause pollution accumulated severely over the urban surface.     

Spatial analysis and land use regression of VOCs and NO2 in Dallas, Texas during two seasons

Passive air sampling for nitrogen dioxide (NO(2)) and select volatile organic compounds (VOCs) was conducted at 24 fire stations and a compliance monitoring site in Dallas, Texas, USA during summer 2006 and winter 2008. This ambient air monitoring network was established to assess intra-urban gradients of air pollutants to evaluate the impact of traffic and urban emissions on air quality. Ambient air monitoring and GIS data from spatially representative fire station sites were collected to assess spatial variability. Pairwise comparisons were conducted on the ambient data from the selected sites based on city section. These weeklong samples yielded NO(2) and benzene levels that were generally higher during the winter than the summer. With respect to the location within the city, the central section of Dallas was generally higher for NO(2) and benzene than north and south. Land use regression (LUR) results revealed spatial gradients in NO(2) and selected VOCs in the central and some northern areas. The process used to select spatially representative sites for air sampling and the results of analyses of coarse- and fine-scale spatial variability of air pollutants on a seasonal basis provide insights to guide future ambient air exposure studies in assessing intra-urban gradients and traffic impacts.     

Evaluation by TCAM Model of Physical–Chemical Properties of Aerosol in Northern Italy

The chemical and size characterisation of aerosol in northern Italy was investigated by means of transport chemical aerosol model (TCAM) multiphase model long-term simulations performed within the frame of the CityDelta-CAFE exercise. The results show a high contribution of secondary inorganic compounds, in particular far from the Milan metropolitan area and in the large rural area of the Po valley where the major chemical fractions are ammonium and nitrates. The sulphate contribution is quite similar throughout this region, with the exception of areas close to SOx point emissions. Primary PM and organic compounds fractions, on the other hand, are greater close to major urban areas, in particular the Milan metropolitan zone. This work also presents a seasonal characterisation analysis of physical and chemical aerosol features. The results show that in winter, due to domestic heating emission of NOx and low temperatures, which favour the formation of secondary nitrate and organic aerosol, the PM10 and PM2.5 concentrations are higher than those in summer, in agreement with experimental data presented in several studies focussing on the same region.     

Can buildings sector achieve the carbon mitigation ambitious goal: Case study for a low-carbon demonstration city in China?

China is committed to peaking its carbon emissions by 2030 and become a carbon-neutral society by 2060. The building sector that accounts for over one-third of the total carbon emissions is expected to face a great challenge in helping China achieve this goal. Shenzhen, as a low-carbon pilot city, whether its low-carbon work of urban buildings reaches the target is crucial. An attempt has been made in this study to assess the intensity of carbon emissions and associated reduction efficiency of urban buildings (operation stage) in Shenzhen by using the life cycle assessment method. The results show that the total carbon emissions generated from the buildings' operation stage have increased from 22 million metric tons (Mt) CO₂eq in 2005 to 42 (±13%) Mt. CO₂eq in 2019. Carbon emissions mainly result from the buildings' electricity use (79%), followed by refrigerant release emissions (12%). The energy conservation and carbon emissions reduction intensity in Shenzhen is at the middle level in China, and there is considerable space for improvement. According to scenario-based analysis, the carbon emission of the buildings sector can probably reach its peak by 2025 with the implementation of suitable policies – 5 years earlier than national target by 2030. Overall, this study makes a systemic analysis of the characteristics of urban buildings energy consumption and carbon emissions reduction, which can provide supportings for justifying the effectiveness of low-carbon activities in Shenzhen and beyond.     

Air-quality Prognosis, for the Implementation of Abatement Strategies over Large Urban Areas

State-of-the-art approaches for urban air-quality characterisation have several drawbacks due to apriori assumptions and/or due to inherent limitations of the concept utilised. For the evaluation of abatement scenarios it is either necessary to embark on extensive monitoring campaigns or to consistently apply numerical models for atmospheric dispersion. The 'ENVISOR' methodology applied here is a mixture of the two approaches. It forecasts pollutant concentrations during real episodes and assesses the impact from the construction of a new highway across a large urban domain of 100×100 km2. Data from an extensive monitoring network are used to identify real modelling periods and for validating the modelling simulations. The selected periods are aiming to the assessment of 'annual mean' or 'episodic' conditions. These periods are short-listed according to the abatement scenario under consideration. This approach yields accurate forecasts for the concentration of pollutants after extensive validation tests extended over the whole domain. It is foreseen that the impact from the highway construction will be minimal for photochemical pollution whereas, higher impact will result for inert pollutants due to additional emissions from the highway.     

区域环境空气质量预报的一般方法和基本原则

为响应公众服务和环境管理对环境空气质量预报的迫切需求,中国环境监测总站率先在京津冀区域开展了区域环境空气质量预报测试。结合目前积累的预报经验和国内外相关研究,探索出区域空气质量预报的一般方法和基本原则,讨论了客观订正环节中大气扩散条件、污染源排放、物理化学过程和空气质量变化规律的分析方法及系统建立、结果确定、天气控制形势分析和信息表述等基本原则,以期为各地开展环境空气质量预报提供技术参考。     

Emission inventory of evaporative emissions of VOCs in four metro cities in India

High concentrations of volatile organic compounds (VOCs) in ambient air of urban areas stress the need for the control of VOC emissions due to the toxic and carcinogenic nature of many VOCs commonly encountered in urban air. Emission inventories are an essential tool in the management of local air quality, which provide a listing of sources of air pollutant emissions within a specific area over a specified period of time. This study intended to provide a level IV emission inventory as par the United States Environmental Protection Agency (USEPA) definition for evaporative VOC emissions in the metro cities of India namely Delhi, Mumbai, Chennai, and Kolkata. The vehicular evaporative emissions are found to be the largest contributor to the total evaporative emissions of hydrocarbons followed by evaporative losses related to petrol loading and unloading activities. Besides vehicle-related activities, other major sources contributing to evaporative emissions of hydrocarbons are surface coating, dry cleaning, graphical art applications, printing (newspaper and computer), and the use of consumer products. Various specific preventive measures are also recommended for reducing the emissions.     

System Dynamics Modeling and Policy Simulation for Urban Traffic: a Case Study in Beijing

This paper constructs a system dynamics model for simulating the impact of different strategies on urban traffic’s energy consumption and carbon emissions. Based on a case study in Beijing, the model includes three subsystems: (1) urban traffic, (2) population and economy, and (3) energy consumption and carbon emissions. First, the model is used to decompose the impact of different vehicles on energy consumption and carbon emissions. Decomposition results show that private cars have the most significant impact on urban traffic’s energy consumption and carbon emissions; however, total vehicle kilometers traveled by private cars are the smallest among four trip modes. Then, the model is used to simulate different urban traffic policies. Policies are categorized as follows: (a) driving restrictions on vehicle registration numbers, (b) a scheme for vehicle registrations via a lottery system, and (c) development of public transportation infrastructures. Scenario simulation results show that all those measures can reduce energy consumption and carbon emissions. Though the last strategy (c) contains several delays, its effect is more stable and far-reaching. Finally, some recommendations about easing traffic pressure and reducing traffic emissions are given.     

江苏省空气质量预报与实测结果比对研究

选取2015年1—8月江苏地区NAQPMS、CMAQ、CAMx、WRF-Chem 4个模式预报结果与实测值进行比对分析,结果表明,标准化分数偏差(MFB)为-0.066 5~0.201 1,标准化分数误差(MFE)最大值为0.381 8,均在理想范围内,其中CAMx预报效果相对较好,WRF-Chem有一定误差。4个模式相比,NAQPMS对于PM_(10)的模拟性能较好,各模式对PM_(2.5)模拟性能相近,CMAQ和CAMx对O_3模拟较好,WRF-Chem对CO模拟较好,各模式对SO_2和NO_2的模拟都需进一步优化。     

基于AGNES算法优化BP神经网络和GIS系统的大气污染物浓度预测

建立了大气污染物浓度与影响因子之间的BP神经网络,对城市中各监测点位的次日大气污染物浓度进行预测,采用GIS的插值分析进行污染物空间分布预测,其中BP神经网络的输入向量采用AGNES算法进行处理。以太原市区SO2、PM10浓度预测为例,选择气温、湿度、降水量、大气压强、风速和前5天的污染物浓度等10个参数训练BP神经网络,结果表明,BP神经网络的训练效果较好,预测结果与实际浓度显著相关,R2分别为0.988、0.976;结合太原市8个监测点位的污染物浓度预测值,运用GIS空间差值法绘出SO2、PM10的浓度分布预测图,该图与实际情况大体符合,并且与国控大气污染企业的分布显著相关,Pearson相关系数分别为0.969、0.949。     

天津市基于新标准的空气质量预报模型效果评估

利用2015年环境空气质量监测数据,对天津市OPAQ空气质量统计预报模型预测效果进行验证评估。结果表明,模型对天津市AQI和PM_(2.5)、PM_(10)、O_3、NO——2的预测结果与实测结果具有较好的趋势一致性,且预测时间越临近,拟合度越好,24 h预报的相关系数r全部达到0.8以上。对PM_(2.5)的预报性能明显优于PM_(10)、O_3和NO_2,PM_(2.5)平均值预测略呈正偏差,但重污染预测值偏低约15%;O_3和NO_2预测值呈明显负偏差,O_3峰值预测不足,NO_2预测值整体偏低,均以24 h预报趋势性最好,但负偏差最为突出。     

An integrated air quality forecast system for a metropolitan area

Air quality forecasting is an important issue in environmental research, due to the effects that air pollutants have on population health. To deal with this topic, in this work an integrated modelling system has been developed to forecast daily maximum eight hours ozone concentrations and daily mean PM10 concentrations, up to two days in advance, over an urban area. The presented approach involves two steps. In the first step, artificial neural networks are identified and applied to get point-wise forecasting. In the second step, the forecasts obtained at the monitoring station locations are spatially interpolated all over the domain using the cokriging technique, which allows to improve the spatial interpolation in the absence of densely sampled data. The integrated modelling system has been then applied to a case study over Northern Italy, performing a validation over space and time for the year 2004 and analyzing if the limit values for the protection of human health set by the European Commission are respected. The presented approach represents a fast and reliable way to provide decision makers and the general public with air quality forecasting, and to support prevention and precautionary measures.