PubMed · 14232149
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P J LAWTHER. 1965. AIR POLLUTION.. https://pubmed.ncbi.nlm.nih.gov/14232149/
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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.
This study was undertaken to determine whether there is an association between air pollutants levels and increased number of hospital admissions for cardiovascular diseases (CVD) in Kaohsiung, Taiwan. Hospital admissions records for CVD and ambient air contaminant data collected from monitoring station in Kaohsiung were obtained for the period 1997-2000. The relative risk of hospital admission for CVD was estimated using a case-crossover approach, controlling for weather variables, day of the week, seasonality, and long-term time trends. In the single-pollutant model, on warm days (> or =25 degrees C) statistically significant positive associations were found between levels of particular matter of < 10 microm aerodynamic diameter (PM10), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3). On cool days (< 25 degrees C), all pollutants except O3 were significantly associated with increased CVD admissions. For the two-pollutant model, CO and O3 were both significant in combination with each of the other four contaminants on warm days. PM10 and NO2 remained significantly associated with elevated CVD admissions on warm days. On cool days, CO and NO2 remained statistically significant in all the two-pollutant models. This study provides evidence that higher levels of ambient contaminants, particularly CO, increase the risk of increased hospital admissions for CVD.
A binary latent variable is constructed to account for the correlation between multiple binary outcomes and is treated as a dependent variable in modelling for covariate effects. This modelling method is similar to the structural equation modelling. Three models are considered: (1) all covariates affecting the binary latent variable directly; (2) some covariates affecting the binary latent variable while other affecting the manifestation of the binary outcomes; and (3) no covariates are included. Gibbs sampling, a special case of the Markov Chain Monte Carlo method, is used to estimate the parameters in the models. Simulation studies show that this method is valid and reliable in estimating covariate effects. But Model (1) fitted the data best with lowest value in the deviance information criteria. The method is illustrated by applying it to the data analysis of an environmental air pollution study. The results show that air pollution (i.e. the most versus the least polluted district) (odds ratio 1.20; 95% confidence interval 0.97-1.49; p=0.102), smoking (relative to not smoking) (2.75; 2.21-3.41; p < 0.001) and mosquito coil use (relative to non-use) (1.27; 0.99-1.62; p=0.058) had an impact on the respiratory health of male adults in Hong Kong.