Air quality in London: evidence of persistence, seasonality and trends.

The poor air quality in the London metropolis has sparked our interest in studying the time series dynamics of air pollutants in the city. The dataset consists of roadside and background air quality for seven standard pollutants: nitric oxide (NO), nitrogen dioxide (NO2), oxides of nitrogen (NOx), ozone (O3), particulate matter (PM10 and PM2.5) and sulphur dioxide (SO2), using fractional integration to investigate issues such as persistence, seasonality and time trends in the data. Though we notice a large degree of heterogeneity across pollutants and a persistent behaviour based on a long memory pattern is observed practically in all cases. Seasonality and decreasing linear trends are also found in some cases. The findings in the paper may serve as a guide to air pollution management and European Union (EU) policymakers.pre-print455 K

Features Exploration from Datasets Vision in Air Quality Prediction Domain

Air pollution and its consequences are negatively impacting on the world population and the environment, which converts the monitoring and forecasting air quality techniques as essential tools to combat this problem. To predict air quality with maximum accuracy, along with the implemented models and the quantity of the data, it is crucial also to consider the dataset types. This study selected a set of research works in the field of air quality prediction and is concentrated on the exploration of the datasets utilised in them. The most significant findings of this research work are: (1) meteorological datasets were used in 94.6% of the papers leaving behind the rest of the datasets with a big difference, which is complemented with others, such as temporal data, spatial data, and so on; (2) the usage of various datasets combinations has been commenced since 2009; and (3) the utilisation of open data have been started since 2012, 32.3% of the studies used open data, and 63.4% of the studies did not provide the data

Environmental Pollution Analysis and Impact Study-A Case Study for the Salton Sea in California

A natural experiment conducted on the shrinking Salton Sea, a saline lake in California, showed that each one foot drop in lake elevation resulted in a 2.6% average increase in PM2.5 concentrations. The shrinking has caused the asthma rate continues to increase among children, with one in five children being sent to the emergency department, which is related to asthma. In this paper, several data-driven machine learning (ML) models are developed for forecasting air quality and dust emission to study, evaluate and predict the impacts on human health due to the shrinkage of the sea, such as the Salton Sea. The paper presents an improved long short-term memory (LSTM) model to predict the hourly air quality (O3 and CO) based on air pollutants and weather data in the previous 5 h. According to our experiment results, the model generates a very good R2 score of 0.924 and 0.835 for O3 and CO, respectively. In addition, the paper proposes an ensemble model based on random forest (RF) and gradient boosting (GBoost) algorithms for forecasting hourly PM2.5 and PM10 using the air quality and weather data in the previous 5 h. Furthermore, the paper shares our research results for PM2.5 and PM10 prediction based on the proposed ensemble ML models using satellite remote sensing data. Daily PM2.5 and PM10 concentration maps in 2018 are created to display the regional air pollution density and severity. Finally, the paper reports Artificial Intelligence (AI) based research findings of measuring air pollution impact on asthma prevalence rate of local residents in the Salton Sea region. A stacked ensemble model based on support vector regression (SVR), elastic net regression (ENR), RF and GBoost is developed for asthma prediction with a good R2 score of 0.978

A deep learning approach for Spatio-Temporal forecasting of new cases and new hospital admissions of COVID-19 spread in Reggio Emilia, Northern Italy

Since February 2020, the COVID-19 epidemic has rapidly spread throughout Italy. Some studies showed an association of environmental factors, such as PM10, PM2.5, NO2, temperature, relative humidity, wind speed, solar radiation and mobility with the spread of the epidemic. In this work, we aimed to predict via Deep Learning the real-time transmission of SARS-CoV-2 in the province of Reggio Emilia, Northern Italy, in a grid with a small resolution (12 km × 12 km), including satellite information

Spatiotemporal and temporal forecasting of ambient air pollution levels through data-intensive hybrid artificial neural network models

Outdoor air pollution (AP) is a serious public threat which has been linked to severe respiratory and cardiovascular illnesses, and premature deaths especially among those residing in highly urbanised cities. As such, there is a need to develop early-warning and risk management tools to alleviate its effects. The main objective of this research is to develop AP forecasting models based on Artificial Neural Networks (ANNs) according to an identified model-building protocol from existing related works. Plain, hybrid and ensemble ANN model architectures were developed to estimate the temporal and spatiotemporal variability of hourly NO2 levels in several locations in the Greater London area. Wavelet decomposition was integrated with Multilayer Perceptron (MLP) and Long Short-term Memory (LSTM) models to address the issue of high variability of AP data and improve the estimation of peak AP levels. Block-splitting and crossvalidation procedures have been adapted to validate the models based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Willmott’s index of agreement (IA). The results of the proposed models present better performance than those from the benchmark models. For instance, the proposed wavelet-based hybrid approach provided 39.15% and 28.58% reductions in RMSE and MAE indices, respectively, on the performance of the benchmark MLP model results for the temporal forecasting of NO2 levels. The same approach reduced the RMSE and MAE indices of the benchmark LSTM model results by 12.45% and 20.08%, respectively, for the spatiotemporal estimation of NO2 levels in one site at Central London. The proposed hybrid deep learning approach offers great potential to be operational in providing air pollution forecasts in areas without a reliable database. The model-building protocol adapted in this thesis can also be applied to studies using measurements from other sites.Outdoor air pollution (AP) is a serious public threat which has been linked to severe respiratory and cardiovascular illnesses, and premature deaths especially among those residing in highly urbanised cities. As such, there is a need to develop early-warning and risk management tools to alleviate its effects. The main objective of this research is to develop AP forecasting models based on Artificial Neural Networks (ANNs) according to an identified model-building protocol from existing related works. Plain, hybrid and ensemble ANN model architectures were developed to estimate the temporal and spatiotemporal variability of hourly NO2 levels in several locations in the Greater London area. Wavelet decomposition was integrated with Multilayer Perceptron (MLP) and Long Short-term Memory (LSTM) models to address the issue of high variability of AP data and improve the estimation of peak AP levels. Block-splitting and crossvalidation procedures have been adapted to validate the models based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Willmott’s index of agreement (IA). The results of the proposed models present better performance than those from the benchmark models. For instance, the proposed wavelet-based hybrid approach provided 39.15% and 28.58% reductions in RMSE and MAE indices, respectively, on the performance of the benchmark MLP model results for the temporal forecasting of NO2 levels. The same approach reduced the RMSE and MAE indices of the benchmark LSTM model results by 12.45% and 20.08%, respectively, for the spatiotemporal estimation of NO2 levels in one site at Central London. The proposed hybrid deep learning approach offers great potential to be operational in providing air pollution forecasts in areas without a reliable database. The model-building protocol adapted in this thesis can also be applied to studies using measurements from other sites

Four-Dimensional Variational Assimilation of Aerosol Data from In-situ and Remote Sensing Platforms

Die Assimilation von Aerosoldaten war bisher im Wesentlichen auf die Verwendung von Messungen der Gesamtmassenkonzentrationen von Partikeln bis zu einer bestimmten Größe und Messungen von optischer Tiefe beschränkt. Das Chemie-Transport-Modell EURAD-IM des Rheinischen Instituts für Umweltforschung (RIU) enhält ein hochentwickeltes vierdimensionales variationales (4D-var) Assimilationssystem für Gasphasenspezies, das nun um eine teilweise adjungierte Version des Aerosol-modells MADE erweitert wurde, um speziesaufgelöste Aerosolmessungen assimilieren zu können. Vorbereitend wurde bereits der äusserst rechenzeitaufwendige Mechanismus zur Lösung der Chemie der sekundären anorganischen Aerosole innerhalb des MADE mithilfe eines I/O-mapping-Verfahrens ersetzt. Der resultierende Algorithmus wurde nun adjungiert und die Funktionalität des adjungierten Aerosoltransportes sichergestellt. Desweiteren wurden verschiedene Beobachtungsoperatoren entwickelt und gleichzeitig adjungiert. Dazu gehören Integrationsroutinen für Massenkonzentrationen und Anzahldichten. Im Rahmen des AERO-SAM Projektes wurde ein Strahlungstransportmodell, Teil eines Satelliten-Retrieval-Systems, in das Modell eingebaut. Die Besonderheit liegt darin, dass das Modell speziesaufgelöste aerosoloptische Tiefen liefert. Das so konstruierte Aerosolassimilationssystem ist auf zwei Episoden angewandt worden. Als erstes auf den Sommer 2003, als ein langanhaltendes Hochdruckgebiet über Europa lag. Diese Wetterlage begünstigte Waldbrände und brachte stark erhöhte Feinstaubbelastung mit sich. In diesem Zeitraum wurde das neue Assimilationssystem getestet und der Nutzen der Assimilation von PM10 insbesondere von speziesaufgelösten Satellitendaten untersucht. Außerdem wurde die ZEPTER-2 Messkampagne aus dem Herbst 2008 ausgewählt. Ein zur Messplatform umgebauter Zeppelin, der mit einem CPC (Condensation Particle Counter) ausgestattet war, hat räumlich und zeitlich hochaufgelöste Partikelanzahldichten gemessen. In dieser Episode wurde der Fokus auf die Assimilation der Anzahldichten sowie der Leistung des Systems auf Modellgittern mit hoher Auflösung gerichtet. In beiden Fällen wurde Anfangswertoptimierung durchgeführt und das System selbst, sowie das Vermögen, die Vorhersage von Aerosolen zu verbessern, untersucht. Es hat sich herausgstellt, dass sich durch Assimilation von Aerosolen eine deutliche Verbesserung der Vorhersage insgesamt erzielen lässt, während durch die Assimilation speziesaufgelöster Retrievals zusätzlich die Zusammensetzung der Aerosole angepasst werden kann