TY - JOUR
T1 - Assessment and prediction of air quality using fuzzy logic and autoregressive models
AU - Carbajal-Hernández, José Juan
AU - Sánchez-Fernández, Luis P.
AU - Carrasco-Ochoa, Jesús A.
AU - Martínez-Trinidad, José Fco
PY - 2012/12
Y1 - 2012/12
N2 - In recent years, artificial intelligence methods have been used for the treatment of environmental problems. This work, presents two models for assessment and prediction of air quality. First, we develop a new computational model for air quality assessment in order to evaluate toxic compounds that can harm sensitive people in urban areas, affecting their normal activities. In this model we propose to use a Sigma operator to statistically asses air quality parameters using their historical data information and determining their negative impact in air quality based on toxicity limits, frequency average and deviations of toxicological tests. We also introduce a fuzzy inference system to perform parameter classification using a reasoning process and integrating them in an air quality index describing the pollution levels in five stages: excellent, good, regular, bad and danger, respectively. The second model proposed in this work predicts air quality concentrations using an autoregressive model, providing a predicted air quality index based on the fuzzy inference system previously developed. Using data from Mexico City Atmospheric Monitoring System, we perform a comparison among air quality indices developed for environmental agencies and similar models. Our results show that our models are an appropriate tool for assessing site pollution and for providing guidance to improve contingency actions in urban areas.
AB - In recent years, artificial intelligence methods have been used for the treatment of environmental problems. This work, presents two models for assessment and prediction of air quality. First, we develop a new computational model for air quality assessment in order to evaluate toxic compounds that can harm sensitive people in urban areas, affecting their normal activities. In this model we propose to use a Sigma operator to statistically asses air quality parameters using their historical data information and determining their negative impact in air quality based on toxicity limits, frequency average and deviations of toxicological tests. We also introduce a fuzzy inference system to perform parameter classification using a reasoning process and integrating them in an air quality index describing the pollution levels in five stages: excellent, good, regular, bad and danger, respectively. The second model proposed in this work predicts air quality concentrations using an autoregressive model, providing a predicted air quality index based on the fuzzy inference system previously developed. Using data from Mexico City Atmospheric Monitoring System, we perform a comparison among air quality indices developed for environmental agencies and similar models. Our results show that our models are an appropriate tool for assessing site pollution and for providing guidance to improve contingency actions in urban areas.
KW - Air quality assessment
KW - Artificial intelligence
KW - Pattern processing
KW - Prediction
UR - http://www.scopus.com/inward/record.url?scp=84863769646&partnerID=8YFLogxK
U2 - 10.1016/j.atmosenv.2012.06.004
DO - 10.1016/j.atmosenv.2012.06.004
M3 - Artículo
SN - 1352-2310
VL - 60
SP - 37
EP - 50
JO - Atmospheric Environment
JF - Atmospheric Environment
ER -