It has been more than 20 years that the Measurement of Pollution in The Troposphere (MOPITT) mission onboard the NASA Terra satellite keeps providing us CO atmospheric concentration measurements around the globe. The current paper observes CO mixing ratio from the MOPITT Version 8 (MOP03J_V008) instrument in order to study the spatiotemporal analysis of CO (spanning from April 2000 to February 2020) in the Troposphere of Iran. Results indicate that the average CO in Iran’s troposphere has been 133.5 ppbv (i.e., 5.5 ppbv lower than the global mean CO). The highest distribution of CO (with an average of 150 ppbv) belongs to the city of Tehran (the capital of Iran) as well as the Caspian Sea coastal area, while the lowest value (with an average of less than 110 ppbv) has been estimated on the Zagros Mountains (southwestern Iran). The highest and lowest CO values have been observed in cold and hot months, respectively. Seasonally speaking, it is also clear that the highest and lowest carbon monoxide values occur in winter and summer, respectively. The vertical profile of MOPITT CO shows the maximum CO concentration at lower levels of the troposphere. It has been expanded up to 150 hPa. The trend is investigated by means of Pearson correlation coefficient statistical method. Overall, long-term monitoring of MOPITT CO in Iran indicates a decreasing trend of tropospheric CO over the 20 years (Y=-0.008X+449.31). Possible reasons for such a decrease can be related to improved transportation fleet, increased fuel quality, plans for traffic control, promotion of heating systems, and promotion of industrial fuels and factories.

Local regressions have been widely employed for decomposing atmospheric data series. However, the use of local quadratic regressions is less extended. The current paper is grounded on the hypothesis that local linear regressions are able to capture CO2 and CH4 temporal evolution equally as well as quadratic linear regressions. Thus, the current paper pursues the following goals: (1) to quantify the temporal variability of both gases by using the local linear and local quadratic regression method; (2) to analyse the main statistics of the detrended series over time; (3) to compare results between the local linear and local quadratic regression method. Minimum mixing ratios in late summer and maximum in winter were detected for both gases. Atmospheric increases of an average of 1.98 ppm year−1 for CO2 and 11 ppb year−1 for CH4 were found when applying the local linear regression method. Alternatively, an increase of 2.24 ppm year−1 for CO2 and around 10.34 ppb year−1 for CH4 was obtained when the local quadratic regression method was applied. The Pearson correlation coefficients (0.21–0.40) showed acceptable values due to the large amount of available data. Statistically significant differences for the initial and the smoothed trend, as well as statistically significant differences for the seasonal component, were reported when comparing the local linear with the local quadratic method. Overall, both methods proved easy to apply and both provided acceptable data accuracy.

In this study, we evaluated atmospheric particulate matter (PM) concentration predictions at a regional scale using a simplified Lagrangian particle dispersion modeling system and the Bayesian and multiplicative ratio correction optimization (Bayesian-RAT) method to improve the mixing ratio forecast of PM₁₀ and PM₂.₅. We first examined the forecast performance of the LPD (i.e., the simplified FLEXPART model combined with the Bayesian-RAT method) by comparing the model predictions with the PM concentration observations from 95 observation stations in Xingtai city and its surrounding areas. The first 2 months (i.e., Oct. and Nov. 2017) of the study period represented the typical spin-up time period, and the analysis period was December 2017. The LPD forecast system was much better (correlation coefficient: R=0.64 vs. 0.48 and 0.67 vs. 0.50 for PM₁₀ and PM₂.₅, respectively; root mean square error: RMSE = 74.98 vs. 105.96 μg/m³ for PM₁₀ and 54.89 vs. 72.81 μg/m³ for PM₂.₅) than the pre-calibration results. We also compared the LPD forecasting model with other models (WRF-Chem and Camx) using data from monitoring stations in Xingtai, China, and the LPD forecasting model had higher accuracy than the other models. In particular, the RMSE scores for hourly PM₁₀ concentrations were reduced by 36.51% and 42.21% compared to WRF-Chem and to Camx, respectively. The PM₂.₅ forecast results, as in the case of PM₁₀, showed a better performance when applying the LPD model to the data from the monitoring stations. The RMSE was reduced by 26.44% and 18.47% relative to the WRF-Chem and Camx, respectively. The results confirm that there is much advantage of the LPD forecast system for predicting PM and may be for other pollutants.