Time series analysis of air pollutants in Beirut, Lebanon

Abstract

This study reports for the first time a time series analysis of daily urban air pollutant levels (CO, NO, NO2, O3, PM10, and SO2) in Beirut, Lebanon. The study examines data obtained between September 2005 and July 2006, and their descriptive analysis shows long-term variations of daily levels of air pollution concentrations. Strong persistence of these daily levels is identified in the time series using an autocorrelation function, except for SO2. Time series of standardized residual values (SRVs) are also calculated to compare fluctuations of the time series with different levels. Time series plots of the SRVs indicate that NO and NO2 had similar temporal fluctuations. However, NO2 and O3 had opposite temporal fluctuations, attributable to weather conditions and the accumulation of vehicular emissions. The effects of both desert dust storms and airborne particulate matter resulting from the Lebanon War in July 2006 are also discernible in the SRV plots.

A Survey on the Knowledge, Attitudes, and Practices of Lebanese Physicians Regarding Air Pollution

“…In our sample, only 60% of physicians agreed that living near an open and well-ventilated intersection is associated with air pollution. Moreover, Lebanon is located in a confined geographical area in the Middle East that is affected by dust storms from the Sahara Desert in Africa and the Arabian Desert in Asia [10]. When dust is transported away from its original source and is mixed with urban air, it elevates the levels of particulate matter in the air and becomes a source of air pollution.…”

“…Instrument: The questionnaire used for the study was developed by a research team that included experts in air pollution, medicine, and survey development (See Appendix A). The questionnaire addresses knowledge of pollutants, sources of air pollution, living and working conditions associated with air pollution (questions 1-7), practices related to the discussion of air pollution with patients (questions 8-9), and attitudes related to the inclusion of air pollution in practice and training (questions [10][11][12][13][14]. Most of the items are rated on five-point Likert scales, with the first two questions requiring yes/no answers.…”

Analyzing Air Pollutant Concentrations in New Delhi, India

“…Various methods for forecasting air pollutant levels and analyzing their impact on weather and human health have been proposed in the literature. Farah et al [5] did a detailed analysis on air pollutants in Beirut, Lebanon to determine their persistence, fluctuations and impact on weather. While Chaudhary et al [6] and Fong et al [7] used recurrent neural networks (RNNs) and longshort-term-memory based networks (LSTMs) respectively to forecast concentration levels of air pollutants, Gul & Khan [8] worked on forecasting hazard level of pollutants using LSTMs.…”

Spatio-Temporal Correlation Analysis of Air Quality in China: Evidence from Provincial Capitals Data

“…It ranges from -1 to 1. We also use the autocorrelation function to explore the time change of the air quality, as the time evolution characteristics of the AQI can be studied by time autocorrelation model [29][30][31][32]. Further, the temporal autocorrelation is estimated on multiple time scales, including the intraday, quarterly and annual analyses.…”