PubMed · 9209844
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A J Cohen. 1997. Replication.. https://pubmed.ncbi.nlm.nih.gov/9209844/
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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.
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PURPOSE: Logistic regression is often used for the analysis of cross-sectional studies, and prevalence odds and odds ratios are obtained. Other methods have been proposed for estimating prevalence ratios. An alternative regression method is also available for estimating rate ratios. Its application to cross-sectional studies is discussed. METHODS: When dealing with chronic conditions, it is possible to model binomial data using the complementary log-log link function log(-log(1-pi)), where pi is the prevalence, an option available on many statistical software packages. In effect, these are models for the disease incidence rate lambda, which is assumed to be constant over the underlying follow-up period t. This approach is based on the well-known relationship 1-pi-exp(-lambda t). The cumulative effect of age on prevalence (effectively "time of follow up") can be accounted for in the model, by specifying it as an offset. RESULTS: The regression coefficients associated with the covariates included in the model estimate rate ratios, rather than odds or prevalence ratios. The method is applied to the analysis of the prevalence of respiratory symptoms in 4395 children aged 7-9 years who are residents of Huddersfield (northern England), surveyed in the framework of the SAVIAH (Small Area Variations of Air Quality and Health) study. CONCLUSIONS: By considering saturated models including only sex as a covariate, direct comparison of crude and fitted parameters (odds, prevalence, and rate ratios) shows that, for short follow-up periods, the complementary log-log model is a valid alternative to logistic regression. More complex models including other covariates are also discussed.