PubMed · 4318163
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J J Gabay, N I Sax. 1968. A simplified technique for stack dispersion mapping with tritium.. https://doi.org/10.1016/0013-9351(68)90006-6
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Capturing the spatiotemporal distribution of pollutants in industrial parks remains challenging for regional air quality models because of their coarse resolution (3 km), resulting in uncertainties in local emission quantification. To address this, we developed the Integrated Multiscale Air Quality Modelling System for Industry (IAQMS-Industry), coupling the regional Nested Air Quality Prediction Modelling System (NAQPMS) with a city-scale chemical transport model. This framework integrates point-source locations and Gaussian plume dispersion to simulate particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) at 100 m resolution. Applied to the Beijing Yi Zhuang and Tangshan industrial parks and evaluated against observations. The coupled model achieved a normalized mean bias (NMB) ranging from 3.1 % to 6.2 %, improving upon NAQPMS (-16.9 % to -7.7 %). Spatial analysis revealed that coarse regional grids underestimated the PM2.5​ concentrations at industrial sites by smoothing gradients, whereas IAQMS-Industry successfully resolved spatial patterns. Industrial point emissions accounted for 22.9 %-26.4 % of PM2.5 in the coupled model, which was significantly greater than the regional model estimates of 1.6 %-13.7 %. These findings indicate that regional models overestimate pollutant dispersion processes in industrial parks while underestimating local industrial impacts. By explicitly resolving point-source dynamics and linking them to regional transport, IAQMS-Industry provides a robust tool for designing targeted emission controls in industrial cities and balancing local air quality improvements with minimized regional pollution outflow. This study underscores the necessity of multiscale modelling for accurate source apportionment and informed environmental governance in industrial zones.
Ethanol is an attractive alternative fuel because it is a renewable bio-based resource and it is oxygenated, thereby providing the potential to reduce particulate emissions in compression-ignition engines. In this review the properties and specifications of ethanol blended with diesel fuel are discussed. Special emphasis is placed on the factors critical to the potential commercial use of these blends. These factors include blend properties such as stability, viscosity and lubricity, safety and materials compatibility. The effect of the fuel on engine performance, durability and emissions is also considered. The formulation of additives to correct certain key properties and maintain blend stability is suggested as a critical factor in ensuring fuel compatibility with engines. However, maintaining vehicle safety with these blends may entail fuel tank modifications. Further work is required in specifying acceptable fuel characteristics, confirming the long-term effects on engine durability, and ensuring safety in handling and storing ethanol-diesel blends.
Journey-time exposures represent an important, though as yet little-studied, component of human exposure to traffic-related air pollution, potentially with important health effects. Methods for assessing journey-time exposures, either as part of epidemiological studies or for policy assessment, are, however, poorly developed. This paper describes the development and testing of a GIS-based system for modeling human journey-time exposures to traffic-related air pollution: STEMS (Space-Time Exposure Modeling System). The model integrates data on source activity, pollutant dispersion, and travel behavior to derive individual- or group-level exposure measures to atmospheric pollution. The model, which is designed to simulate exposures of people as they move through a changing air pollution field, was developed, validated, and trialed in Northampton, UK. The system currently uses ArcInfo to couple four separate submodels: a source activity/emission model (SATURN), a proprietary atmospheric dispersion model (ADMS-Urban), an empirically derived background air pollution model, and a purposely designed time-activity-based exposure model (TOTEM). This paper describes the structure of the modeling system; presents results of field calibration, validation, and sensitivity analysis; and illustrates the use of the model to analyze journey-time exposures of schoolchildren.