PubMed · 13873473
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R O BROOKS. Monitoring techniques for toxic materials.. https://pubmed.ncbi.nlm.nih.gov/13873473/
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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.
The case-crossover design uses cases only, and compares exposures just prior to the event times to exposures at comparable control, or 'referent' times, in order to assess the effect of short-term exposure on the risk of a rare event. It has commonly been used to study the effect of air pollution on the risk of various adverse health events. Proper selection of referents is crucial, especially with air pollution exposures, which are shared, highly seasonal, and often have a long-term time trend. Hence, careful referent selection is important to control for time-varying confounders, and in order to ensure that the distribution of exposure is constant across referent times, a key assumption of this method. Yet the referent strategy is important for a more basic reason: the conditional logistic regression estimating equations commonly used are biased when referents are not chosen a priori and are functions of the observed event times. We call this bias in the estimating equations overlap bias. In this paper, we propose a new taxonomy of referent selection strategies in order to emphasize their statistical properties. We give a derivation of overlap bias, explore its magnitude, and consider how the bias depends on properties of the exposure series. We conclude that the bias is usually small, though highly unpredictable, and easily avoided.
Source apportionment of urban fine particle mass (PM(2.5)) was performed from data collected during 1998-1999 in Amsterdam (The Netherlands), Erfurt (Germany) and Helsinki (Finland), using principal component analysis (PCA) and multiple linear regression. Six source categories of PM(2.5) were identified in Amsterdam. They were traffic-related particles (30% of the average PM(2.5)), secondary particles (34%), crustal material (7%), oil combustion (11%), industrial and incineration processes (9%), and sea salt (2%). The unidentified PM(2.5) fraction was 7% on the average. In Erfurt, four source categories were extracted with some difficulties in interpretation of source profiles. They were combustion emissions related to traffic (32%), secondary PM (32%), crustal material (21%) and industrial processes (8%). In Erfurt, 3% of PM(2.5) remained unidentified. Air pollution data and source apportionment results from the two Central European cities were compared to previously published results from Helsinki, where about 80% of average PM(2.5) was attributed to transboundary air pollution and particles from traffic and other regional combustion sources. Our results indicate that secondary particles and local combustion processes (mainly traffic) were the most important source categories in all cities; their impact on the average PM(2.5) was almost equal in Amsterdam and Erfurt whereas, in Helsinki, secondary particles made up for as much as half of the total average PM(2.5).