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Abstract. Knowledge of the sources of carbonaceous aerosol affecting air quality in Riyadh, Saudi Arabia, is limited but needed for the development of pollution control strategies. We conducted sampling of PM 2.5 from April to September 2012 at various sites in the city and used a thermo-optical semi-continuous method to quantify the organic carbon (OC) and elemental carbon (EC) concentrations. The average OC and EC concentrations were 4.7 ± 4.4 and 2.1 ± 2.5 µg m −3 , respectively, during this period. Both OC and EC concentrations had strong diurnal variations, with peaks at 06:00-08:00 LT and 20:00-22:00 LT, attributed to the combined effect of increased vehicle emissions during rush hour and the shallow boundary layer in the early morning and at night. This finding suggested a significant influence of local vehicular emissions on OC and EC. The OC / EC ratio in primary emissions was estimated to be 1.01, close to documented values for diesel emissions. Estimated primary organic carbon (POC) and secondary organic carbon (SOC) concentrations were comparable, with average concentrations of 2.0 ± 2.4 and 2.8 ± 3.4 µg m −3 , respectively.We also collected 24 h samples of PM 10 onto quartz microfiber filters and analyzed these for an array of metals by inductively coupled plasma atomic emission spectroscopy (ICP-AES). Total OC was correlated with Ca (R 2 of 0.63), suggesting that OC precursors and Ca may have similar sources, and the possibility that they underwent similar atmospheric processing. In addition to a ubiquitous dust source, Ca is emitted during desalting processes in the numerous refineries in the region and from cement kilns, suggesting these sources may also contribute to observed OC concentrations in Riyadh. Concentration weighted trajectory (CWT) analysis showed that high OC and EC concentrations were associated with air masses arriving from the Persian Gulf and the region around Baghdad, locations with high densities of oil fields and refineries as well as a large Saudi Arabian cement plant. We further applied positive matrix factorization to the aligned dataset of EC, OC, and metal concentrations (Al, Ca, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, and V). Three factors were derived and were proposed to be associated with oil combustion, industrial emissions (Pb based), and a combined source from oil fields, cement production, and local vehicular emissions. The dominant OC and EC source was the combined source, contributing 3.9 µg m −3 (80 %) to observed OC and 1.9 µg m −3 (92 %) to observed EC.

CMAQ was implemented in the central region of Saudi Arabia and the effect of simulating models using various chemical mechanisms on selected oxidants, nitrogen species, and O 3 was investigated. CB05TUCL predicted OH, MEPX, and NO z about 7%, 7.7%, and 8% more than CB05E51 respectively; however, there was no observable difference in the O 3 predictions. The differences in variations of SAPRC07 mechanism (SAPRC07TB, SAPRC07TC, and SAPRC07TIC) for all parameters were less than 1%. RACM2 produced the highest OH and H 2 O 2 concentrations. RACM2 enhanced OH production in the range of 24% -32% and H 2 O 2 by 9% over other mechanisms; these are comparatively less than the findings of other studies. Similarly, CB05 produced over 40% more PAN concentration than CB05. Moreover, PAN concentrations produced by all mechanisms were very high compared to other studies. SAPRC07 produced approximately 3% more mean surface O 3 concentration than RACM2 and approximately 10% more than CB05. RACM2 O 3 predictions were higher than CB05 by 7%. The predicted O 3 concentrations by CB05, RACM2, and SAPRC07 were 6%, 11%, and 15% more than the average observed concentrations, which indicate that closest predictions to the observed values were by CB05. This study concludes that there is a wide variation of mechanisms with respect to the predictions of oxidants and nitrogen compounds; however, less variation is noticed in predictions of O 3 . For any air pollution control strategies and photochemical modeling studies in the current region or in any other arid regions, the CB05 mechanism is recommended.

This paper proposes a simple method of optimizing Air Quality Monitoring Network (AQMN) using Geographical Information System (GIS), interpolation techniques and historical data. Existing air quality stations are systematically eliminated and the missing data are filled in using the most appropriate interpolation technique. The interpolated data are then compared with the observed data. Pre-defined performance measures root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (r) were used to check the accuracy of the interpolated data. An algorithm was developed in GIS environment and the process was simulated for several sets of measurements conducted in different locations in Riyadh, Saudi Arabia. This methodology proves to be useful to the decision makers to find optimal numbers of stations that are needed without compromising the coverage of the concentrations across the study area.

ABSTRACT:The prediction and presentation of weather and marine conditions in an integrated way is of paramount importance in many areas of interest, including offshore operations. Huge datasets which are generated from running weather and marine forecasting systems present a challenge in terms of processing and presenting these data efficiently. This paper presents a method of optimizing huge meteorological and marine datasets for effective use by GIS, creation of automatic meteorological and marine maps, and an interactive map for querying any necessary integrated meteorological and marine data at selected geographical locations. This application significantly reduces the data processing time and the complexities in presenting such huge datasets.