Abstract
Particulate air pollution has aggravated cardiovascular and lung diseases. Accurate and constant air quality forecasting on a local scale facilitates the control of air pollution and the design of effective strategies to limit air pollutant emissions. CAMS provides 4-day-ahead regional (EU) forecasts in a 10 km spatial resolution, adding value to the Copernicus EO and delivering open-access consistent air quality forecasts. In this work, we evaluate the CAMS PM forecasts at a local scale against in-situ measurements, spanning 2 years, obtained from a network of stations located in an urban coastal Mediterranean city in Greece. Moreover, we investigate the potential of modelling techniques to accurately forecast the spatiotemporal pattern of particulate pollution using only open data from CAMS and calibrated low-cost sensors. Specifically, we compare the performance of the Analog Ensemble (AnEn) technique and the Long Short-Term Memory (LSTM) network in forecasting PM2.5 and PM10 concentrations for the next four days, at 6 h increments, at a station level. The results show an underestimation of PM2.5 and PM10 concentrations by a factor of 2 in CAMS forecasts during winter, indicating a misrepresentation of anthropogenic particulate emissions such as wood-burning, while overestimation is evident for the other seasons. Both AnEn and LSTM models provide bias-calibrated forecasts and capture adequately the spatial and temporal variations of the ground-level observations reducing the RMSE of CAMS by roughly 50% for PM2.5 and 60% for PM10. AnEn marginally outperforms the LSTM using annual verification statistics. The most profound difference in the predictive skill of the models occurs in winter, when PM is elevated, where AnEn is significantly more efficient. Moreover, the predictive skill of AnEn degrades more slowly as the forecast interval increases. Both AnEn and LSTM techniques are proven to be reliable tools for air pollution forecasting, and they could be used in other regions with small modifications.
Highlights
Air pollution is a global pivotal issue in the fields of health and environment, affecting at the same time both the economy and social life
We evaluate the copernicus atmosphere monitoring service (CAMS) Particulate matter (PM) forecasts at a local scale against in-situ measurements, spanning 2 years, obtained from a dense network of calibrated low-cost air pollution stations located in an urban coastal Mediterranean city in Greece
The analysis is assessed with the following statistical parameters [50]: (a) the probability of detection (POD), which corresponds to the ratio of the observations forecasted correctly by the method; (b) the false alarm ratio (FAR), representing the proportion of events which are categorized incorrectly as extremes events; (c) the miss mate (MIS), which is a complement score of POD and indicates the extreme events that are not forecasted; (d) the critical success index (CSI) that gives the overall skill of a model to detect correctly an extreme event, in consideration of both false alarms and misses
Summary
Air pollution is a global pivotal issue in the fields of health and environment, affecting at the same time both the economy and social life. Expediting industrialization and urbanization triggered an increase in cardiovascular and lung diseases, attributable to air pollution [1]. Particulate matter (PM) consists of a complex mixture of particles with major components sulfate, nitrates, ammonia, sodium chloride, black carbon, mineral dust and water [2]. Both coarse particulate matter (PM10) and fine particles (PM2.5), due to their diminutive size, can penetrate deeply into the respiratory system, causing serious chronic health problems including airway irritation, asthma, irregular heart rate, abnormal lung function [3,4]. Particle pollution in urban areas is significantly higher due to the accumulation of primary sources of PM2.5 and PM10, including industrial and vehicle emissions, fuel oils and indoor activities [7]
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