Abstract
Abstract. An accurate emission inventory is a crucial part of air pollution management and is essential for air quality modelling. One source in an emission inventory, an industrial source, has been known with high uncertainty in both location and magnitude in China. In this study, a new reallocation method based on blue-roof industrial buildings was developed to replace the conventional method of using population density for the Chinese emission development. The new method utilized the zoom level 14 satellite imagery (i.e. Google®) and processed it based on hue, saturation, and value (HSV) colour classification to derive new spatial surrogates for province-level reallocation, providing more realistic spatial patterns of industrial PM2.5 and NO2 emissions in China. The WRF-CMAQ-based PATH-2016 model system was then applied with the new processed industrial emission input in the MIX inventory to simulate air quality in the Greater Bay Area (GBA) area (formerly called Pearl River Delta, PRD). In the study, significant root mean square error (RMSE) improvement was observed in both summer and winter scenarios in 2015 when compared with the population-based approach. The average RMSE reductions (i.e. 75 stations) of PM2.5 and NO2 were found to be 11 µg m−3 and 3 ppb, respectively. Although the new method for allocating industrial sources did not perform as well as the point- and area-based industrial emissions obtained from the local bottom-up dataset, it still showed a large improvement over the existing population-based method. In conclusion, this research demonstrates that the blue-roof industrial allocation method can effectively identify scattered industrial sources in China and is capable of downscaling the industrial emissions from regional to local levels (i.e. 27 to 3 km resolution), overcoming the technical hurdle of ∼ 10 km resolution from the top-down or bottom-up emission approach under the unified framework of emission calculation.
Highlights
The emission inventory is essential for air quality management and climate studies
Three urban areas, Jing–Jin–Ji (Baoding area with 332 km2), Yangtze River Delta (Shanghai area with 1336 km2), and Greater Bay Area (GBA) (Fushan area with 1194 km2), were picked as the training dataset as we recognized that cities and regions might have their own building styles and development patterns, choosing these three regions allowed more diverse samples to be included in the training dataset and incorporated the potential effect of solar incident angles on image colour under different latitudinal positions and time of satellite passing
We developed a new method called the “blueroof allocation method” for assigning industrial emissions for the gridded air quality simulation
Summary
The emission inventory is essential for air quality management and climate studies. Various applications, including setting up regional emission reduction targets and performing numerical air quality forecasts, rely upon an accurate inventory for sound assessment and judgement (Krzyzanowski, 2009; Zhao et al, 2015). In developed countries like the USA, industrial sources are usually large, but they do not always contribute to a dominant portion in the emission inventory (e.g. 10 %–15 % in PM10 and 25 %–60 % in NMVOC), and the data collection process is commonly incorporated into routine permitting exercise, making it easy to be included in their national inventory (ECCC, 2017; Janssens-Maenhout et al, 2015; Lam et al, 2004). This is not the case for developing countries like China where industrial sources are considered as a major emitter; Li et al (2017) reported that
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