بررسي مولفه هاي زماني غلظت آلاينده ي منوكسيدكربن و پارامتر هاي هواشناسي در شهر تهران

Study of Temporal Components of Carbon Monoxide Concentration and Meteorological Variables in Tehran

آلاينده‌ي منوكسيدكربن co به‌عنوان يكي از آلاينده‌هاي معيار، يكي از بحراني‌ترين آلاينده‌ها به لحاظ شاخص سلامت هوا در تهران بوده است. در اين پژوهش به بررسي بخش‌هاي زماني اين آلاينده و نحوه‌ي ارتباط آن‌ها با پارامترهاي هواشناسي توسط فيلتر K‌Z پرداخته شده است. نتايج اين بررسي نشان مي‌دهد در طي سال‌هاي مورد بررسي، غلظت منوكسيدكربن با كاهش از مقدار p‌p‌m 11 به p‌p‌m 5 روندي كاهشي و پارامترهاي دماي هوا، جهت، و شدت باد روندي افزايشي داشته‌اند. علاوه بر اين، مولفه‌ي بلندمدت با سهمي حدود كمتر از 10%، كمترين سهم از تغييرات سري زماني را شامل مي‌شود و از طرفي بيشترين ميزان وابستگي را در داده‌هاي مختلف دارد. بر اين اساس، غلظت منوكسيدكربن با پارامترهاي جهت باد و رطوبت نسبي بيشترين ميزان همبستگي را دارد. با مشخص‌شدن اين دو پارامتر، امكان بررسي روند تغييرات انتشار منوكسيدكربن فراهم مي‌شود.

Carbon monoxide is one of the most important pollutants. It is a serious pollutant in terms of the air quality index (AQI) found in Tehran before 2000. Local meteorological conditions and transport systems are the strongest factors for changing pollution levels. Because of some restrictions in this paper, we discuss the effects of meteorological variables on CO concentration. Examining the trend of this pollutant and the effects of meteorological variables in a comprehensive plan to reduce air pollution is described in this paper. In order to discern changes in CO data, it is necessary to separate their different temporal components. Hence, study of the CO time series and its temporal components is needed. Also, meteorological signals must be removed in order to make better air quality management decisions for the future. Various methods have been developed for decomposing the time series into long-term (overall emission, pollutant transport, climate and policy related), seasonal (solar cycle induced), and short-term (weather related) components. These techniques are PEST, anomalies, wavelet transform, and the Kolmogorov–Zurbenko (KZ) filter. Kolmogorov–Zurbenko, as a low-passed filter, can be applied to datasets with missing data, and is much easier to use than the other methods, with acceptable precision. In this paper, it is used to separate the temporal components of average hourly CO and meteorological variables between 2000 and 2008. Results of this study indicate that CO has decreased from 11 ppm to 5 ppm, while temperature and wind vectors have an increasing trend in the years studied. One of the most possible reasons for a decrease in CO is improvement in the transport system. Moreover, long term components contribute less than 10 percent to the total variance of the time series. Long term components of CO concentration and meteorological variables have more correlation than other temporal components. Accordingly, CO concentration has strong correlative contributions with wind vector and relative humidity. These results are useful for examining emission-related CO trends in Tehran.