Spatial Representativeness Error in the Ground-Level Observation Networks for Black Carbon Radiation Absorption.

There is high uncertainty in the direct radiative forcing of black carbon (BC), an aerosol that strongly absorbs solar radiation. The observation-constrained estimate, which is several times larger than the bottom-up estimate, is influenced by the spatial representativeness error due to the mesoscale inhomogeneity of the aerosol fields and the relatively low resolution of global chemistry-transport models. Here we evaluated the spatial representativeness error for two widely used observational networks (AErosol RObotic NETwork and Global Atmosphere Watch) by downscaling the geospatial grid in a global model of BC aerosol absorption optical depth to 0.1° × 0.1°. Comparing the models at a spatial resolution of 2° × 2° with BC aerosol absorption at AErosol RObotic NETwork sites (which are commonly located near emission hot spots) tends to cause a global spatial representativeness error of 30%, as a positive bias for the current top-down estimate of global BC direct radiative forcing. By contrast, the global spatial representativeness error will be 7% for the Global Atmosphere Watch network, because the sites are located in such a way that there are almost an equal number of sites with positive or negative representativeness error.

A multi-scale health impact assessment of air pollution over the 21st century.

BACKGROUND: Ozone and PM2.5 are current risk factors for premature death all over the globe. In coming decades, substantial improvements in public health may be achieved by reducing air pollution. To better understand the potential of emissions policies, studies are needed that assess possible future health impacts under alternative assumptions about future emissions and climate across multiple spatial scales. METHOD: We used consistent climate-air-quality-health modeling framework across three geographical scales (World, Europe and Ile-de-France) to assess future (2030-2050) health impacts of ozone and PM2.5 under two emissions scenarios (Current Legislation Emissions, CLE, and Maximum Feasible Reductions, MFR). RESULTS: Consistently across the scales, we found more reductions in deaths under MFR scenario compared to CLE. 1.5 [95% CI: 0.4, 2.4] million CV deaths could be delayed each year in 2030 compared to 2010 under MFR scenario, 84% of which would occur in Asia, especially in China. In Europe, the benefits under MFR scenario (219000 CV deaths) are noticeably larger than those under CLE (109,000 CV deaths). In Ile-de-France, under MFR more than 2830 annual CV deaths associated with PM2.5 changes could be delayed in 2050 compared to 2010. In Paris, ozone-related respiratory mortality should increase under both scenarios. CONCLUSION: Multi-scale HIAs can illustrate the difference in direct consequences of costly mitigation policies and provide results that may help decision-makers choose between different policy alternatives at different scales.

Exposure to ambient black carbon derived from a unique inventory and high-resolution model.

Black carbon (BC) is increasingly recognized as a significant air pollutant with harmful effects on human health, either in its own right or as a carrier of other chemicals. The adverse impact is of particular concern in those developing regions with high emissions and a growing population density. The results of recent studies indicate that BC emissions could be underestimated by a factor of 2-3 and this is particularly true for the hot-spot Asian region. Here we present a unique inventory at 10-km resolution based on a recently published global fuel consumption data product and updated emission factor measurements. The unique inventory is coupled to an Asia-nested (∼50 km) atmospheric model and used to calculate the global population exposure to BC with fully quantified uncertainty. Evaluating the modeled surface BC concentrations against observations reveals great improvement. The bias is reduced from -88% to -35% in Asia when the unique inventory and higher-resolution model replace a previous inventory combined with a coarse-resolution model. The bias can be further reduced to -12% by downscaling to 10 km using emission as a proxy. Our estimated global population-weighted BC exposure concentration constrained by observations is 2.14 µg⋅m(-3); 130% higher than that obtained using less detailed inventories and low-resolution models.

Using a chemistry transport model to account for the spatial variability of exposure concentrations in epidemiologic air pollution studies.

Environmental epidemiology and more specifically time-series analysis have traditionally used area-averaged pollutant concentrations measured at central monitors as exposure surrogates to associate health outcomes with air pollution. However, spatial aggregation has been shown to contribute to the overall bias in the estimation of the exposure-response functions. This paper presents the benefit of adding features of the spatial variability of exposure by using concentration fields modeled with a chemistry transport model instead of monitor data and accounting for human activity patterns. On the basis of county-level census data for the city of Paris, France, and a Monte Carlo simulation, a simple activity model was developed accounting for the temporal variability between working and evening hours as well as during transit. By combining activity data with modeled concentrations, the downtown, suburban, and rural spatial patterns in exposure to nitrogen dioxide, ozone, and PM2.5 (particulate matter [PM] < or = 10 microm in aerodynamic diameter) were captured and parametrized. Exposures predicted with this model were used in a time-series study of the short-term effect of air pollution on total nonaccidental mortality for the 4-yr period from 2001 to 2004. It was shown that the time series of the exposure surrogates developed here are less correlated across co-pollutants than in the case of the area-averaged monitor data. This led to less biased exposure-response functions when all three co-pollutants were inserted simultaneously in the same regression model. This finding yields insight into pollutant-specific health effects that are otherwise masked by the high correlation among co-pollutants.