Burden of Mortality and Disease Attributable to Multiple Air Pollutants in Warsaw, Poland.

Air pollution is a significant public health issue all over the world, especially in urban areas where a large number of inhabitants are affected. In this study, we quantify the health burden due to local air pollution for Warsaw, Poland. The health impact of the main air pollutants, PM, NOX, SO₂, CO, C₆H₆, BaP and heavy metals is considered. The annual mean concentrations are predicted with the CALPUFF air quality modeling system using the year 2012 emission and meteorological data. The emission field comprises point, mobile and area sources. The exposure to these pollutants was estimated using population data with a spatial resolution of 0.5 × 0.5 km². Changes in mortality and in disability-adjusted life-years (DALYs) were estimated with relative risk functions obtained from literature. It has been predicted that local emissions cause approximately 1600 attributable deaths and 29,000 DALYs per year. About 80% of the health burden was due to exposure to fine particulate matter (PM2.5). Mobile and area sources contributed 46% and 52% of total DALYs, respectively. When the inflow from outside was included, the burden nearly doubled to 51,000 DALYs. These results indicate that local decisions can potentially reduce associated negative health effects, but a national-level policy is required for reducing the strong environmental impact of PM emissions

A High-Definition Spatially Explicit Modeling Approach for National Greenhouse Gas Emissions from Industrial Processes: Reducing the Errors and Uncertainties in Global Emission Modeling

Spatially-explicit (gridded) emission inventories (EIs) should allow us to analyse sectoral emissions patterns to estimate potential impacts of emission policies and support decisions on reducing emissions. However, such EIs are often based on simple downscaling of national level emissions estimate and the changes in subnational emissions distributions are not necessarily reflecting the actual changes driven by the local emissions drivers. This article presents a high definition,100m resolution bottom-up inventory of greenhouse gas (GHG) emissions from the industrial processes (fuel combustion activities in energy and manufacturing industry, fugitive emissions, mineral products, chemical industry, metal production, food and drink) that is exemplified on data for Poland. We propose an improved emission disaggregation algorithmthat fully utilizes a collection of activity data available at national/provincial level to the level of individual point and diffused (area) emission sources. To ensure the accuracy of the resulting 100m emission fields, the geospatial data used for mapping emission sources (point source geolocation and land cover classification) were subject to thorough human visual inspection.The resulting 100m emission field even hold cadastres of emissions separately for each industrial emission category, while we start with IPCC-compliant national sectoral GHG estimates that we made using Polish official statistics. We aggregated the resulting emissions to the level of administrative units such as municipalities, districts and provinces. We also compiled cadastres in regular grids and then compared them with EDGAR results. Quantitative analysis of discrepancies between both results revealed quite frequent misallocations of point sources used in the EDGAR compilation that considerably deteriorates high resolution inventories. We also propose a Monte-Carlo method-based uncertainty assessment that yields a detailed estimation of the GHG emission uncertainty in the main categories of the analysed processes. We found that the above mentioned geographical coordinates and patterns used for emission disaggregation have the greatest impact on overall uncertainty of GHG inventoriesfrom the industrial processes

Spatial disaggregation of pollutant concentration data

The purpose of this study is to develop a method for allocating pollutant concentrations to finer spatial scales conditional on covariate information observable in a fine grid. Spatial dependence is modeled with the conditional autoregressive structure. The maximum likelihood approach to inference is employed, and the optimal predictors are developed to assess missing concentrations in a fine grid. The method is developed for a practical application of an output from the dispersion model CALPUFF run for Warsaw agglomeration