An approach for estimating the health effects of changes over time in air pollution: an illustration using cardio-respiratory hospital admissions in London

Air pollution and climate change as grand challenges to sustainability

There is a cross-disciplinary link between air pollution, climate crisis, and sustainable lifestyle as they are the most complex struggles of the present century. This review takes an in-depth look at this relationship, considering carbon dioxide emissions primarily from the burning of fossil fuels as the main contributor to global warming and focusing on primary SLCPs such as methane and ground-level ozone. Such pollutants severely alter the climate through the generation of greenhouse gases. The discussion is extensive and includes best practices from conventional pollution control technologies to hi-tech alternatives, including electric vehicles, the use of renewables, and green decentralized solutions. It also addresses policy matters, such as imposing stricter emissions standards, setting stronger environmental regulations, and rethinking some economic measures. Besides that, new developments such as congestion charges, air ionization, solar-assisted cleaning systems, and photocatalytic materials are among the products discussed. These strategies differ in relation to the local conditions and therefore exhibit a varying effectiveness level, but they remain evident as a tool of pollution deterrence. This stresses the importance of holistic and inclusive approach in terms of engineering, policies, stakeholders, and ecological spheres to tackle.

A review on the role of dispersion and receptor models in asthma research

There is substantial evidence that air pollution exposure is associated with asthma prevalence that affects millions of people worldwide. Air pollutant exposure can be determined using dispersion models and refined with receptor models. Dispersion models offer the advantage of giving spatially distributed outdoor pollutants concentration while the receptor models offer the source apportionment of specific chemical species. However, the use of dispersion and/or receptor models in asthma research requires a multidisciplinary approach, involving experts on air quality and respiratory diseases. Here, we provide a literature review on the role of dispersion and receptor models in air pollution and asthma research, their limitations, gaps and the way forward. We found that the methodologies used to incorporate atmospheric dispersion and receptor models in human health studies may vary considerably, and several of the studies overlook features such as indoor air pollution, model validation and subject pathway between indoor spaces. Studies also show contrasting results of relative risk or odds ratio for a health outcome, even using similar methodologies. Dispersion models are mostly used to estimate air pollution levels outside the subject's home, school or workplace; however, very few studies addressed the subject's routines or indoor/outdoor relationships. Conversely, receptor models are employed in regions where asthma incidence/prevalence is high or where a dispersion model has been previously used for this assessment. Road traffic (vehicle exhaust) and NOx are found to be the most targeted source and pollutant, respectively. Other key findings were the absence of a standard indicator, shortage of studies addressing VOC and UFP, and the shift toward chemical speciation of exposure.

Change in risk of hospital admissions for ischemic heart disease after the implementation of a mass rapid transit system in Taipei

Numerous epidemiologic studies demonstrated an association between an increase in levels of fine particles (particulate matter less than 2.5 um in diameter, PM_2.5) and elevation in the number of hospital admissions for cardiovascular diseases. Air pollution levels including PM_2.5 clearly decreased in Taipei City after the mass rapid transit (MRT) system began operations in 1996. The aim of this study was to investigate the extent of changes in the risk of daily hospital admissions for ischemic heart disease (IHD) over a 17-year period after the installation of a MRT system in Taipei. The full study was divided into Period 1 (1997-2000), total track length 65.1 km; Period 2 (2001-2008), total track length 75.8 km; and Period 3 (2009-2013), total track length 121.3 km. A time-stratified case-crossover analysis was conducted to estimate relative risk (RR) of hospital admissions for IHD for each 10 ug/m³ increase in PM_2.5 for different periods. On cool days, the associated RR of IHD for Period 3 was consistently lower compared to period 2 in both our single- and two-pollutant models. However, the daily risk for IHD admissions was found to be significantly higher for period 3 compared to period 2 in our single-pollutant model and in our two-pollutant models (PM_2.5+ SO₂) on warm days. The basis for this difference is unknown. Data suggests that an MRT system may provide substantial health benefits, a finding that may be helpful to urban communities, urban planners, and public health specialists.

Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis

BACKGROUND

The time stratified case cross-over approach is a popular alternative to conventional time series regression for analysing associations between time series of environmental exposures (air pollution, weather) and counts of health outcomes. These are almost always analyzed using conditional logistic regression on data expanded to case-control (case crossover) format, but this has some limitations. In particular adjusting for overdispersion and auto-correlation in the counts is not possible. It has been established that a Poisson model for counts with stratum indicators gives identical estimates to those from conditional logistic regression and does not have these limitations, but it is little used, probably because of the overheads in estimating many stratum parameters.

METHODS

The conditional Poisson model avoids estimating stratum parameters by conditioning on the total event count in each stratum, thus simplifying the computing and increasing the number of strata for which fitting is feasible compared with the standard unconditional Poisson model. Unlike the conditional logistic model, the conditional Poisson model does not require expanding the data, and can adjust for overdispersion and auto-correlation. It is available in Stata, R, and other packages.

RESULTS

By applying to some real data and using simulations, we demonstrate that conditional Poisson models were simpler to code and shorter to run than are conditional logistic analyses and can be fitted to larger data sets than possible with standard Poisson models. Allowing for overdispersion or autocorrelation was possible with the conditional Poisson model but when not required this model gave identical estimates to those from conditional logistic regression.

CONCLUSIONS

Conditional Poisson regression models provide an alternative to case crossover analysis of stratified time series data with some advantages. The conditional Poisson model can also be used in other contexts in which primary control for confounding is by fine stratification.

Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis

BACKGROUND

The time stratified case cross-over approach is a popular alternative to conventional time series regression for analysing associations between time series of environmental exposures (air pollution, weather) and counts of health outcomes. These are almost always analyzed using conditional logistic regression on data expanded to case-control (case crossover) format, but this has some limitations. In particular adjusting for overdispersion and auto-correlation in the counts is not possible. It has been established that a Poisson model for counts with stratum indicators gives identical estimates to those from conditional logistic regression and does not have these limitations, but it is little used, probably because of the overheads in estimating many stratum parameters.

METHODS

The conditional Poisson model avoids estimating stratum parameters by conditioning on the total event count in each stratum, thus simplifying the computing and increasing the number of strata for which fitting is feasible compared with the standard unconditional Poisson model. Unlike the conditional logistic model, the conditional Poisson model does not require expanding the data, and can adjust for overdispersion and auto-correlation. It is available in Stata, R, and other packages.

RESULTS

By applying to some real data and using simulations, we demonstrate that conditional Poisson models were simpler to code and shorter to run than are conditional logistic analyses and can be fitted to larger data sets than possible with standard Poisson models. Allowing for overdispersion or autocorrelation was possible with the conditional Poisson model but when not required this model gave identical estimates to those from conditional logistic regression.

CONCLUSIONS

Conditional Poisson regression models provide an alternative to case crossover analysis of stratified time series data with some advantages. The conditional Poisson model can also be used in other contexts in which primary control for confounding is by fine stratification.

Influence of human activity patterns, particle composition, and residential air exchange rates on modeled distributions of PM2.5 exposure compared with central-site monitoring data

Central-site monitors do not account for factors such as outdoor-to-indoor transport and human activity patterns that influence personal exposures to ambient fine-particulate matter (PM(2.5)). We describe and compare different ambient PM(2.5) exposure estimation approaches that incorporate human activity patterns and time-resolved location-specific particle penetration and persistence indoors. Four approaches were used to estimate exposures to ambient PM(2.5) for application to the New Jersey Triggering of Myocardial Infarction Study. These include: Tier 1, central-site PM(2.5) mass; Tier 2A, the Stochastic Human Exposure and Dose Simulation (SHEDS) model using literature-based air exchange rates (AERs); Tier 2B, the Lawrence Berkeley National Laboratory (LBNL) Aerosol Penetration and Persistence (APP) and Infiltration models; and Tier 3, the SHEDS model where AERs were estimated using the LBNL Infiltration model. Mean exposure estimates from Tier 2A, 2B, and 3 exposure modeling approaches were lower than Tier 1 central-site PM(2.5) mass. Tier 2A estimates differed by season but not across the seven monitoring areas. Tier 2B and 3 geographical patterns appeared to be driven by AERs, while seasonal patterns appeared to be due to variations in PM composition and time activity patterns. These model results demonstrate heterogeneity in exposures that are not captured by the central-site monitor.

One way coupling of CMAQ and a road source dispersion model for fine scale air pollution predictions

In this paper we have coupled the CMAQ and ADMS air quality models to predict hourly concentrations of NO _X , NO₂ and O₃ for London at a spatial scale of 20 m × 20 m. Model evaluation has demonstrated reasonable agreement with measurements from 80 monitoring sites in London. For NO₂ the model evaluation statistics gave 73% of the hourly concentrations within a factor of two of observations, a mean bias of -4.7 ppb and normalised mean bias of -0.17, a RMSE value of 17.7 and an r value of 0.58. The equivalent results for O₃ were 61% (FAC2), 2.8 ppb (MB), 0.15 (NMB), 12.1 (RMSE) and 0.64 (r). Analysis of the errors in the model predictions by hour of the week showed the need for improvements in predicting the magnitude of road transport related NO _X emissions as well as the hourly emissions scaling in the model. These findings are consistent with recent evidence of UK road transport NO _X emissions, reported elsewhere. The predictions of wind speed using the WRF model also influenced the model results and contributed to the daytime over prediction of NO _X concentrations at the central London background site at Kensington and Chelsea. An investigation of the use of a simple NO-NO₂-O₃ chemistry scheme showed good performance close to road sources, and this is also consistent with previous studies. The coupling of the two models raises an issue of emissions double counting. Here, we have put forward a pragmatic solution to this problem with the result that a median double counting error of 0.42% exists across 39 roadside sites in London. Finally, whilst the model can be improved, the current results show promise and demonstrate that the use of a combination of regional scale and local scale models can provide a practical modelling tool for policy development at intergovernmental, national and local authority level, as well as for use in epidemiological studies.