A new detailed chemical model for indoor air pollution

A detailed chemical box model has been constructed based on a comprehensive chemical mechanism (the Master Chemical Mechanism) to investigate indoor air chemistry in a typical urban residence in the UK. Unlike previous modelling studies of indoor air chemistry, the mechanism adopted contains no simplifications such as lumping or the use of surrogate species, allowing more insight into indoor air chemistry than previously possible. The chemical mechanism, which has been modified to include the degradation reactions of key indoor air pollutants, contains around 15,400 reactions and 4700 species. The results show a predicted indoor OH radical concentration up to 4.0×10⁵ molecule cm⁻³, only a factor of 10–20 less than typically observed outdoors and sufficient for significant chemical cycling to take place. Concentrations of PAN-type species and organic nitrates are found to be important indoors, reaching concentrations of a few ppb. Sensitivity tests highlight that the most crucial parameters for modelling the concentration of OH are the light-intensity levels and the air exchange rate. Outdoor concentrations of O₃ and NO_X are also important in determining radical concentrations indoors. The reactions of ozone with alkenes and monoterpenes play a major role in producing new radicals, unlike outdoors where photolysis reactions are pivotal radical initiators. In terms of radical propagation, the reaction of HO₂ with NO has the most profound influence on OH concentrations indoors. Cycling between OH and RO₂ is dominated by reaction with the monoterpene species, whilst alcohols play a major role in converting OH to HO₂. Surprisingly, the absolute reaction rates are similar to those observed outdoors in a suburban environment in the UK during the summer. The results from this study highlight the importance of tailoring a model for its particular location and the need for future indoor air measurements of radical species, nitrated species such as PANs and organic nitrates, photolysis rates of key species over the range of wavelengths observed indoors and concurrent measurements of outdoor air pollutant concentrations.     

A model for interprovincial air pollution control based on futures prices

Based on the current status of research on tradable emission rights futures, this paper introduces basic market-related assumptions for China’s interprovincial air pollution control problem. The authors construct an interprovincial air pollution control model based on futures prices: the model calculated the spot price of emission rights using a classic futures pricing formula, and determined the identities of buyers and sellers for various provinces according to a partitioning criterion, thereby revealing five trading markets. To ensure interprovincial cooperation, a rational allocation result for the benefits from this model was achieved using the Shapley value method to construct an optimal reduction program and to determine the optimal annual decisions for each province. Finally, the Beijing–Tianjin–Hebei region was used as a case study, as this region has recently experienced serious pollution. It was found that the model reduced the overall cost of reducing SO₂ pollution. Moreover, each province can lower its cost for air pollution reduction, resulting in a win–win solution. Adopting the model would therefore enhance regional cooperation and promote the control of China’s air pollution.

Implications: The authors construct an interprovincial air pollution control model based on futures prices. The Shapley value method is used to rationally allocate the cooperation benefit. Interprovincial pollution control reduces the overall reduction cost of SO₂. Each province can lower its cost for air pollution reduction by cooperation.     

A spatial multicriteria model for determining air pollution at sample locations

Atmospheric pollution in urban centers has been one of the main causes of human illness related to the respiratory and circulatory system. Efficient monitoring of air quality is a source of information for environmental management and public health. This study investigates the spatial patterns of atmospheric pollution using a spatial multicriteria model that helps target locations for air pollution monitoring sites. The main objective was to identify high-priority areas for measuring human exposures to air pollutants as they relate to emission sources. The method proved to be viable and flexible in its application to various areas.

Implications:?Spatial multicriteria models provide a tool for air pollution management in urban areas. Analytic hierarchy process (AHP) modeling can help with the process of prioritizing monitoring site locations and minimizing costs.     

A Gaussian puff model with optimal interpolation for air pollution modelling assessment

A Gaussian plume model was modified to simulate the dispersion of non-reactive air pollutants under non-homogeneous wind conditions through a multi-puff approach. It was applied to the city of Lisbon and evaluated by comparison with measured sulphur dioxide data, showing a reasonable skill to estimate the transport and dispersion of pollutants under complex wind field and different atmospheric conditions. The modelling results were integrated with observed data, based on correlation functions determined from historical values, to obtain the improved analytical results by using optimal interpolation. A significant improvement over the predictions by the Gaussian puff model alone was achieved.     

The CCMS air pollution model intercomparison study

Four air pollution transport models were tested and compared in an area of ~ 400 × 400 km². Three models were Eulcrian grid models, the fourth a Lagrangian trajectory model. The data base (emissions and meteorological observations) were essentially the same for all models. Differences in model output could only be a result of the different (numerical) structure of the models and of the differences in processing of the meteorological data. It turned out that the latter was the major source of differences in model results. Generally there was a satisfactory correlation between model results and observed concentrations. Mainly due to the negligence of transport of pollutants into the modelling region, predicted concentrations were considerably lower than the observed.     

A hybrid plume model for local-scale atmospheric dispersion

This paper presents a local-scale dispersion model, based on atmospheric boundary layer scaling theory. In the vicinity of the source, Gaussian equations are used in both the horizontal and vertical directions. After a specified transition distance, gradient transfer theory is applied in the vertical direction, while the horizontal dispersion is still assumed to be Gaussian. The dispersion parameters and eddy diffusivity are modelled in a form, which facilitates the use of a meteorological pre-processor. We present a novel model of the vertical eddy diffusivity (K_z), which is a continuous function of height in various atmospheric scaling regions. The model also includes a treatment of the dry deposition of gases and particulate matter. The accuracy of the numerical model was analysed by comparing the model predictions with two analytical solutions; the numerical deviations from these solutions were less than 2% for the computational regime. The model has been tested against the Kincaid experimental field data. The agreement of the predictions and the data is good on the average, although the internal variation of the predictions versus data scatter plot is substantial.     

A plume blight visibility model for regulatory use

The ERT visibility model has been developed over the past two years to estimate, principally for regulatory purposes, plume blight and visibility impairment resulting from point sources. The model combines the concentration fields calculated from single- or multiple-source dispersion models with calculations of the radiative transfer at four wavelengths in the visual range. It accounts for the scattering and/or absorption of the reactive pollutants NO₂, NO₃⁻, SO₄²⁻ and carbonaceous and non-carbonaceous TSP. It can consider any geometrically specified line of sight (e.g., to a specific vista from a point in a Class I area). The model gives the following information about plume blight visibility degradation: (1) visual range reduction; (2) plume contrast; (3) object contrast degradation; (4) plume discoloration, expressed as a blue/red intensity ratio and (5) object and sky discoloration, expressed as a ΔE value.     

Coupling GEOS-CHEM with a regional air pollution model for Greece

The sensitivity of regional air quality modeling simulations to boundary conditions over Greece is investigated, for various synoptic conditions. For this purpose, a global to mesoscale model-chain is developed and applied, coupling the individual models' simulations. The global chemical transport model GEOS-CHEM, applied in a one-way nested procedure, is used to drive the regional UAM-V chemical dispersion model with time-varying lateral and top boundary conditions. The results of the coupling procedure are compared with the MINOS campaign measurements at Finokalia (Southern Greece) during the period from 1 to 16 August 2001 which is mainly characterized by an interchange of two synoptic types, High-Low and Long Wave trough.The comparison between the simulation results and the measurements reveals that the coupling procedure captures satisfactorily the range of observed CO concentrations at the southern part of Greece. The most severe deviations are observed under strongly variable atmospheric circulation, when no distinct synoptic circulation is allowed to be established in the area. Regarding O₃, the highest, though underestimated, surface concentrations are simulated under Long Wave trough conditions due to the influence of the ozone inflow predicted by GEOS-CHEM at the western boundary of the innermost domain and/or under enhanced NOy emissions arriving at Finokalia from urban and ships plumes.     

基于DPM模型求解自由下落颗粒羽流卷吸空气量

为深入了解自由下落颗粒羽流卷吸空气的特性,对氧化铝颗粒从出口直径为5 mm的圆形孔中自由下落的气固两相流场进行了数值模拟研究。依据RNG k-ε模型求解气相雷诺时均N-S方程并进行离散化处理,采用DPM模型跟踪单个粒子以求解颗粒的运动方程,并通过SIMPLE双向耦合,对自由下落颗粒羽流流动特性进行数值模拟。结果表明：自由下落颗粒羽流卷吸空气气相速度在羽流中心线上处于最大值,随着径向距离的增大速度值明显地减小,直到距离中心线30 mm处,之后速度的变化趋于稳定;颗粒在净质量力作用下沿质量力方向首先作加速运动,之后下落速度up随着下落高度的变化趋于平缓,羽流末端颗粒运动基本遵守颗粒自由沉降规律;羽流卷吸空气量随着下落高度的增加而增大,其规律与颗粒群模型给出的结果高度吻合。通过比较分析发现,采用DPM模型很好的解决了对颗粒相运动方程的求解并可获取颗粒运动轨迹,对于自由下落颗粒羽流特性的分析不失为一种有效而可行的方法。     

An enhanced ozone forecasting model using air mass trajectory analysis

An enhanced ozone forecasting model using nonlinear regression and an air mass trajectory parameter has been developed and field tested. The model performed significantly better in predicting daily maximum 1-h ozone concentrations during a five-year model calibration period (1993–1997) than did a previously reported regression model. This was particularly true on the 28 “high ozone” days ([O₃]>120 ppb) during the period, for which the mean absolute error (MAE) improved from 21.7 to 12.1 ppb. On the 77 days meteorologically conducive to high ozone, the MAE improved from 12.2 to 9.1 ppb, and for all 580 calibration days the MAE improved from 9.5 to 8.35 ppb. The model was field-tested during the 1998 ozone season, and performed about as expected. Using actual meteorological data as input for the ozone predictions, the MAE for the season was 11.0 ppb. For the daily ozone forecasts, which used meteorological forecast data as input, the MAE was 13.4 ppb. The high ozone days were all anticipated by the ozone forecasters when the model was used for next day forecasts.     

Progress in developing an ANN model for air pollution index forecast

An air pollution index (API) reporting system is introduced to selected cities of China for public communication on air quality data. Shanghai is the first city in China providing daily average API reports and forecasts. This paper describes the development of an artificial neural network (ANN) model for the API forecasting in Shanghai. It is a multiple layer perceptron (MLP) network, with meteorological forecasting data as the main input, to output the next day average API values. However, the initial version of the MLP model did not work well. To improve the model, a series of tests were conducted with respect to the training method and structure optimization. Based on the test results, the training algorithm was modified and a new model was built. The new model is now being used in Shanghai for API forecasting. Its performance is shown reasonably well in comparison with observation. The application of the old model was only weakly correlated with observation. In 1-year application, the correlation coefficients were 0.2314, 0.1022 and 0.1710 for TSP, SO₂ and NO_x, respectively. But for the new model, for over 8 months application, the correlation coefficients are raised to 0.6056, 0.6993 and 0.6300 for PM₁₀, SO₂, and NO₂. Further, the new algorithm does not rely on manpower intervention so that it is now being applied in several other Chinese cities with quite different meteorological conditions. The structure of the model and the application results are presented in this paper and also the problems to be further studied.     

An interprovincial cooperative game model for air pollution control in China

The noncooperative air pollution reduction model (NCRM) that is currently adopted in China to manage air pollution reduction of each individual province has inherent drawbacks. In this paper, we propose a cooperative air pollution reduction game model (CRM) that consists of two parts: (1) an optimization model that calculates the optimal pollution reduction quantity for each participating province to meet the joint pollution reduction goal; and (2) a model that distribute the economic benefit of the cooperation (i.e., pollution reduction cost saving) among the provinces in the cooperation based on the Shapley value method. We applied the CRM to the case of SO₂ reduction in the Beijing–Tianjin–Hebei region in China. The results, based on the data from 2003–2009, show that cooperation helps lower the overall SO₂ pollution reduction cost from 4.58% to 11.29%. Distributed across the participating provinces, such a cost saving from interprovincial cooperation brings significant benefits to each local government and stimulates them for further cooperation in pollution reduction. Finally, sensitivity analysis is performed using the year 2009 data to test the parameters’ effects on the pollution reduction cost savings.

Implications: China is increasingly facing unprecedented pressure for immediate air pollution control. The current air pollution reduction policy does not allow cooperation and is less efficient. In this paper we developed a cooperative air pollution reduction game model that consists of two parts: (1) an optimization model that calculates the optimal pollution reduction quantity for each participating province to meet the joint pollution reduction goal; and (2) a model that distributes the cooperation gains (i.e., cost reduction) among the provinces in the cooperation based on the Shapley value method. The empirical case shows that such a model can help improve efficiency in air pollution reduction. The result of the model can serve as a reference for Chinese government pollution reduction policy design.