Abstract
Health of every living being is dependent on level of pollutant in the environment, weather condition, etc, which are interrelated and influencing each other. Presence of pollution particles in high percentage such as particulate matter of size 10 and 2.5 (PM10, PM25) microns in creates higher risk in lung functionality, respiratory disease and reduces cardio functionality; also affects the quality of water sources. Rainfall, technically known as precipitation, occur at non season also create health hazards, seasonal diseases. In our previous work, the relationship between precipitation and pollution were analysed; it is observed that pollution attribute particulate matter, PM2.5, influences precipitation. Pollution data as well as numerical weather data are time series data in nature. This research work analyses the time series data (day wise observations) of pollution and weather, recorded at Chennai city at the year 2017. ARIMA model is constructed after applying required transformations on underlying data. Trend and seasonality found in underlying time series data set are unique since time series components are data-dependent and autocorrelated with previous day observations. Improved forecasting of PM2.5 and precipitation are obtained through ARIMA modelling.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
