Development of a machine learning approach for local-scale ozone forecasting: application to Kennewick, WA

Chemical transport models (CTMs) are widely used for air quality forecasts, but these models require large computational resources and often suffer from a systematic bias that leads to missed poor air pollution events. For example, a CTM-based operational forecasting system for air quality over the Pacific Northwest, called AIRPACT, uses over 100 processors for several hours to provide 48-h forecasts daily, but struggles to capture unhealthy O(3) episodes during the summer and early fall, especially over Kennewick, WA. This research developed machine learning (ML) based O(3) forecasts for Kennewick, WA to demonstrate an improved forecast capability. We used the 2017-2020 simulated meteorology and O(3) observation data from Kennewick as training datasets. The meteorology datasets are from the Weather Research and Forecasting (WRF) meteorological model forecasts produced daily by the University of Washington. Our ozone forecasting system consists of two ML models, ML1 and ML2, to improve predictability: ML1 uses the random forest (RF) classifier and multiple linear regression (MLR) models, and ML2 uses a two-phase RF regression model with best-fit weighting factors. To avoid overfitting, we evaluate the ML forecasting system with the 10-time, 10-fold, and walk-forward cross-validation analysis. Compared to AIRPACT, ML1 improved forecast skill for high-O(3) events and captured 5 out of 10 unhealthy O(3) events, while AIRPACT and ML2 missed all the unhealthy events. ML2 showed better forecast skill for less elevated-O(3) events. Based on this result, we set up our ML modeling framework to use ML1 for high-O(3) events and ML2 for less elevated O(3) events. Since May 2019, the ML modeling framework has been used to produce daily 72-h O(3) forecasts and has provided forecasts via the web for clean air agency and public use: http://ozonematters.com/. Compared to the testing period, the operational forecasting period has not had unhealthy O(3) events. Nevertheless, the ML modeling framework demonstrated a reliable forecasting capability at a selected location with much less computational resources. The ML system uses a single processor for minutes compared to the CTM-based forecasting system using more than 100 processors for hours

Machine learning-based ozone and PM2.5 forecasting: application to multiple AQS sites in the Pacific Northwest

Air quality in the Pacific Northwest (PNW) of the U.S has generally been good in recent years, but unhealthy events were observed due to wildfires in summer or wood burning in winter. The current air quality forecasting system, which uses chemical transport models (CTMs), has had difficulty forecasting these unhealthy air quality events in the PNW. We developed a machine learning (ML) based forecasting system, which consists of two components, ML1 (random forecast classifiers and multiple linear regression models) and ML2 (two-phase random forest regression model). Our previous study showed that the ML system provides reliable forecasts of O(3) at a single monitoring site in Kennewick, WA. In this paper, we expand the ML forecasting system to predict both O(3) in the wildfire season and PM2.5 in wildfire and cold seasons at all available monitoring sites in the PNW during 2017–2020, and evaluate our ML forecasts against the existing operational CTM-based forecasts. For O(3), both ML1 and ML2 are used to achieve the best forecasts, which was the case in our previous study: ML2 performs better overall (R(2) = 0.79), especially for low-O(3) events, while ML1 correctly captures more high-O(3) events. Compared to the CTM-based forecast, our O(3) ML forecasts reduce the normalized mean bias (NMB) from 7.6 to 2.6% and normalized mean error (NME) from 18 to 12% when evaluating against the observation. For PM2.5, ML2 performs the best and thus is used for the final forecasts. Compared to the CTM-based PM2.5, ML2 clearly improves PM2.5 forecasts for both wildfire season (May to September) and cold season (November to February): ML2 reduces NMB (−27 to 7.9% for wildfire season; 3.4 to 2.2% for cold season) and NME (59 to 41% for wildfires season; 67 to 28% for cold season) significantly and captures more high-PM2.5 events correctly. Our ML air quality forecast system requires fewer computing resources and fewer input datasets, yet it provides more reliable forecasts than (if not, comparable to) the CTM-based forecast. It demonstrates that our ML system is a low-cost, reliable air quality forecasting system that can support regional/local air quality management

Control of Toxic Chemicals in Puget Sound, Phase 3: Study of Atmospheric Deposition of Air Toxics to the Surface of Puget Sound

The results of the Phase 1 Toxics Loading study suggested that runoff from the land surface and atmospheric deposition directly to marine waters have resulted in considerable loads of contaminants to Puget Sound (Hart Crowser et al. 2007). The limited data available for atmospheric deposition fluxes throughout Puget Sound was recognized as a significant data gap. Therefore, this study provided more recent or first reported atmospheric deposition fluxes of PAHs, PBDEs, and select trace elements for Puget Sound. Samples representing bulk atmospheric deposition were collected during 2008 and 2009 at seven stations around Puget Sound spanning from Padilla Bay south to Nisqually River including Hood Canal and the Straits of Juan de Fuca. Revised annual loading estimates for atmospheric deposition to the waters of Puget Sound were calculated for each of the toxics and demonstrated an overall decrease in the atmospheric loading estimates except for polybrominated diphenyl ethers (PBDEs) and total mercury (THg). The median atmospheric deposition flux of total PBDE (7.0 ng/m2/d) was higher than that of the Hart Crowser (2007) Phase 1 estimate (2.0 ng/m2/d). The THg was not significantly different from the original estimates. The median atmospheric deposition flux for pyrogenic PAHs (34.2 ng/m2/d; without TCB) shows a relatively narrow range across all stations (interquartile range: 21.2- 61.1 ng/m2/d) and shows no influence of season. The highest median fluxes for all parameters were measured at the industrial location in Tacoma and the lowest were recorded at the rural sites in Hood Canal and Sequim Bay. Finally, a semi-quantitative apportionment study permitted a first-order characterization of source inputs to the atmosphere of the Puget Sound. Both biomarker ratios and a principal component analysis confirmed regional data from the Puget Sound and Straits of Georgia region and pointed to the predominance of biomass and fossil fuel (mostly liquid petroleum products such as gasoline and/or diesel) combustion as source inputs of combustion by-products to the atmosphere of the region and subsequently to the waters of Puget Sound