A 24-h forecast of ozone peaks and exceedance levels using neural classifiers and weather predictions

A neural network combined to a neural classifier is used in a real time forecasting of hourly maximum ozone in the centre of France, in an urban atmosphere. This neural model is based on the MultiLayer Perceptron (MLP) structure. The inputs of the statistical network are model output statistics of the weather predictions from the French National Weather Service. With this neural classifier, the Success Index of forecasting is 78% whereas it is from 65% to 72% with the classical MLPs. During the validation phase, in the Summer of 2003, six ozone peaks above the threshold were detected. They actually were seven

METHODOLOGICAL CONSIDERATION ON PRE-PROCESSING DATA OPTIMIZATION CONCERNING AIR DISPERSION MODEL AND NEURAL NETWORKS: A CASE-STUDY OF OZONE PREDICTION LEVEL

This work analyzes the results of a Neural Network model applied to air pollution data. In particular, we forecast ozone pollutants levels in a short term using both air dispersion models and neural network methods. The purpose of this work is to provide a novel methodological procedure to analyze environmental data by using a neural net as forecast technique for ozone levels in the urban area of Rome. Results show that the model performance can be improved by pre-processing input data using typical datamining techniques and coupling air dispersion model with neural net

METHODOLOGICAL CONSIDERATION ON PRE-PROCESSING DATA OPTIMIZATION CONCERNING AIR DISPERSION MODEL AND NEURAL NETWORKS: A CASE-STUDY OF OZONE PREDICTION LEVEL

This work analyzes the results of a Neural Network model applied to air pollution data. In particular, we forecast ozone pollutants levels in a short term using both air dispersion models and neural network methods. The purpose of this work is to provide a novel methodological procedure to analyze environmental data by using a neural net as forecast technique for ozone levels in the urban area of Rome. Results show that the model performance can be improved by pre-processing input data using typical datamining techniques and coupling air dispersion model with neural net

Neural network forecast of daily pollution concentration using optimal meteorological data at synoptic and local scales

AbstractWe present a simple neural network and data pre–selection framework, discriminating the most essential input data for accurately forecasting the concentrations of PM10, based on observations for the years between 2002 and 2006 in the metropolitan region of Lisbon, Portugal. Starting from a broad panoply of different data sets collected at several air quality and meteorological stations, a forward stepwise regression procedure is applied enabling to automatically identify the most important variables for predicting the pollutant and also to rank them in order of importance. The importance of this variable ranking is discussed, showing that it is very sensitive to the urban location where measurements are obtained. Additionally, the importance of Circulation Weather Types is highlighted, characterizing synoptic scale circulation patterns and the concentration of pollutants. We then quantify the performance of linear and non–linear neural network models when applied to PM10 concentrations. In the light of contradictory results of previous studies, our results show no clear superiority for the case studied of non–linear models over linear models. While all models show similar predictive performances, we find important differences in false alarm rates and demonstrate the importance of removing weekly cycles from input variables

Prediction of hourly ozone concentrations with multiple regression and multilayer perceptron models

In this work ozone observations of an urban area of the east coast of the Iberian Peninsula, are analyzed. The data set contains measurements from five automatic air pollution monitoring stations (background suburban or traffic urban). The application of multiple linear regression and neural networks models is considered. These models forecast hourly ozone levels for short-term prediction intervals (1, 8, and 24 h in advance). The study period is 2010 2012. The input variables are meteorological observations, ozone and nitrogen oxides concentrations, and daily and weekly seasonal cycles. The performance criteria to evaluate the computations accuracy are the residual mean square error, the mean absolute error, and the correlation coefficient between observations and predictions. These criteria have better results for the 1-h and 24-h predictions in all the locations. The comparison of multiple linear regressions and multilayer perceptron networks indicates that the second approach allows to obtain more accurate forecast for the three prediction intervals.Capilla, C. (2016). Prediction of hourly ozone concentrations with multiple regression and multilayer perceptron models. International Journal of Sustainable Development and Planning. 11(4):558-565. doi:10.2495/SDP-V11-N4-558-565S55856511

Statistical and machine learning modelling of UK surface ozone

In addition to atmospheric observations, numerical models are crucial to understand the impacts of human activities on the environment, from attributing poor air quality to assessing climate change impacts. While process-based models, such as chemistry transport models (CTMs), are widely used, recent data science advances enable greater use of statistical and machine learning methods as alternatives to describe and predict atmospheric composition. State-of-the-art data science methods can be faster to run than CTMs and used at high temporal and spatial resolutions due to codebase efficiencies. This thesis focuses on modelling UK surface ozone and its drivers (high levels of which are detrimental to human and plant health) through the development and novel application of sophisticated statistical and machine learning techniques. Motivated by possible adverse effect of climate change on ozone concentrations, a temperature-dependent Extreme Value Analysis is used to explore the probability, magnitude, and frequency of extreme ozone events over recent decades. For 2010–2019, it is found that the 1-year return level of daily maximum 8-h mean (MDA8) ozone exceeds the ‘moderate’ health threshold (100 µg/m3) at >90% of sites, but that the probability of extreme ozone events has markedly decreased since the 1980s. A machine learning methodology to downscale and bias correct a CTM (EMEP4UK) ozone surface was developed and evaluated. Compared to the unadjusted CTM, the downscaled surface exhibits a lower bias in reproducing MDA8 ozone allowing more robust assessments of important policy metrics. Analysis of the downscaled product (2014–2018) reveals on average 27% of the UK fails the government long-term objective for MDA8 ozone to not exceed 100 µg/m3 more than 10 times per year, compared to 99% in the unadjusted CTM. A classification-based machine learning analysis into high-level ozone drivers was also performed and shows a robust relationship between ozone and temperature. The method is demonstrated to offer remarkable promise as a tool with which to forecast the presence of high-level ozone. Despite a UK focus, the data-driven methods developed and applied here are applicable to modelling ozone in other regions of the world where measurements exist

Identification of significant factors for air pollution levels using a neural network based knowledge discovery system

Artificial neural network (ANN) is a commonly used approach to estimate or forecast air pollution levels, which are usually assessed by the concentrations of air contaminants such as nitrogen dioxide, sulfur dioxide, carbon monoxide, ozone, and suspended particulate matters (PMs) in the atmosphere of the concerned areas. Even through ANN can accurately estimate air pollution levels they are numerical enigmas and unable to provide explicit knowledge of air pollution levels by air pollution factors (e.g. traffic and meteorological factors). This paper proposed a neural network based knowledge discovery system aimed at overcoming this limitation in ANN. The system consists of two units: a) an ANN unit, which is used to estimate the air pollution levels based on relevant air pollution factors; b) a knowledge discovery unit, which is used to extract explicit knowledge from the ANN unit. To demonstrate the practicability of this neural network based knowledge discovery system, numerical data on mass concentrations of PM2.5 and PM1.0, meteorological and traffic data measured near a busy traffic road in Hangzhou city were applied to investigate the air pollution levels and the potential air pollution factors that may impact on the concentrations of these PMs. Results suggest that the proposed neural network based knowledge discovery system can accurately estimate air pollution levels and identify significant factors that have impact on air pollution levels

A review of artificial neural network models for ambient air pollution prediction

Research activity in the field of air pollution forecasting using artificial neural networks (ANNs) has increased dramatically in recent years. However, the development of ANN models entails levels of uncertainty given the black-box nature of ANNs. In this paper, a protocol by Maier et al. (2010) for ANN model development is presented and applied to assess journal papers dealing with air pollution forecasting using ANN models. The majority of the reviewed works are aimed at the long-term forecasting of outdoor PM10, PM2.5, and oxides of nitrogen, and ozone. The vast majority of the identified works utilised meteorological and source emissions predictors almost exclusively. Furthermore, ad-hoc approaches are found to be predominantly used for determining optimal model predictors, appropriate data subsets and the optimal model structure. Multilayer perceptron and ensemble-type models are predominantly implemented. Overall, the findings highlight the need for developing systematic protocols for developing powerful ANN models