Abstract
Abstract. Mapping Air Pollution eMissions (MAPM) is a 2-year project whose goal is to develop a method to infer particulate matter (PM) emissions maps from in situ PM concentration measurements. Central to the functionality of MAPM is an inverse model. The input of the inverse model includes a spatially distributed prior emissions estimate and PM measurement time series from instruments distributed across the desired domain. In this proof-of-concept study, we describe the construction of this inverse model, the mathematics underlying the retrieval of the resultant posterior PM emissions maps, the way in which uncertainties are traced through the MAPM processing chain, and plans for future developments. To demonstrate the capability of the inverse model developed for MAPM, we use the PM2.5 measurements obtained during a dedicated winter field campaign in Christchurch, New Zealand, in 2019 to infer PM2.5 emissions maps on a city scale. The results indicate a systematic overestimation in the prior emissions for Christchurch of at least 40 %–60 %, which is consistent with some of the underlying assumptions used in the composition of the bottom-up emissions map used as the prior, highlighting the uncertainties in bottom-up approaches for estimating PM2.5 emissions maps.
Highlights
The growth of mega-cities from global urbanization has degraded urban air quality sufficiently to impede economic growth and create a public health hazard (Adams et al, 2015)
Since this study focuses on constraining the emissions within the boundary of the city of Christchurch, the concentration measurements being provided as input to the inverse model should only reflect the emissions coming from within the boundary of the city; i.e. they should exclude any contribution from emissions sources located outside the city
Using PM2.5 data from the outdoor dust information nodes (ODINs) corresponding to each automatic weather stations (AWSs) site, and the respective 5 min resolution wind data, we select all PM2.5 measurements that correspond to the range of wind directions that define air flowing into the domain
Summary
The growth of mega-cities from global urbanization has degraded urban air quality sufficiently to impede economic growth and create a public health hazard (Adams et al, 2015). While LPDMs are widely used in atmospheric inversion studies for estimating regional fluxes, i.e. emissions (Maksyutov et al, 2020), there are not many studies that have used LPDMs to infer air pollution sources at city scales. Their study focused on predicting PM2.5 concentrations for the purpose of forecasting rather than obtaining the best estimates of PM2.5 emissions to identify source regions Application of their method demonstrated that inferred PM2.5 emissions aggregated over Xuzhou (11 258 km2) were 10 % higher than what was expected from a multi-scale emissions inventory. The main objective of this study is to develop and test an urban inverse model for PM2.5 emissions This inverse model incorporates the in situ measurements recorded during the MAPM field campaign in 2019 in conjunction with atmospheric transport model output and an inventory-based first-guess estimate of PM2.5 emissions to create an optimized emissions estimate for the city. We will describe the methodology of this proof-of-concept study and present the posterior emissions maps including uncertainties for Christchurch, NZ
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