OBJECTIVES: To compare gravimetric measurements with existing networks that rely on beta-attenuation measurements in a desert climate; determine the annual levels of PM2.5 and PM10 over a two-year period in Kuwait; assess compliance with air quality standards; and identify and quantify PM2.5 sources.
METHODS: We custom-designed particle samplers that can withstand large quantities of dust without their inlet becoming overloaded. The samplers were placed in two populated residential locations, one in Kuwait City and another near industrial and petrochemical facilities in Ali Sabah Al-Salem (ASAS) to collect PM2.5 and PM10 samples for mass and elemental analysis. We used positive matrix factorization to identify PM2.5 sources and apportion their contributions.
RESULTS: We collected 2339 samples during the period October 2017 through October 2019. The beta-attenuation method in measuring PM2.5 consistently exceeded gravimetric measurements, especially during dust events. The annual levels for PM2.5 in Kuwait City and ASAS were 41.6 ± 29.0 and 47.5 ± 27.6 μg/m3, respectively. Annual PM2.5 levels in Kuwait were nearly four times higher than the U.S. National Ambient Air Quality Standard. Regional pollution was a major contributor to PM2.5 levels in both locations accounting for 44% in Kuwait City and 46% in ASAS. Dust storms and re-suspended road dust were the second and third largest contributors to PM2.5, respectively.
CONCLUSIONS: The premise that frequent and extreme dust storms make air quality regulation futile is dubious. In this comprehensive particulate pollution analysis, we show that the sizeable regional anthropogenic particulate sources warrant national and regional mitigation strategies to ensure compliance with air quality standards.
METHOD: A naturalistic exploratory un-obstructive observational approach was used in assessing this phenomenon. The relationship between motorcyclists' behaviors and motorcyclists' observed demographic characteristics, the locality of the intersection, time of the week and presence of pillion passengers were analyzed. Chi-Square test of independence was used to establish the statistically significant relationships between dependent and independent variables.
RESULTS: In all, 2,225 motorcyclists and 744 pillion passengers were observed. The results revealed that 33.1% of the motorcyclists ran a red light with 45.4% not using a helmet. Red-light running at signalized intersections was significantly linked to the locality of the intersection, time of the week, and helmet use. The helmet use was low and significantly associated with the presence of a pillion passenger and whether the pillion passenger used a helmet or not.
CONCLUSION: Red-light running is influenced by locality of intersection, time of the week and helmet use. Efforts to reduce red-light running and improve helmet use should involve road safety education, awareness creation, and enforcement of traffic laws by the officials of the National Road Safety Authority and Motor Transport and Traffic Department of the Ghana Police Service. City managers in other low and middle-income countries can use the findings in the study to inform policy.
METHOD: A generalized linear model (GLM) estimates the relationships between different travel mode indicators (e.g., length of motorway per inhabitants, number of motorcycles per inhabitant, percentage of daily trips on foot and by bicycle, percentage of daily trips by public transport) and the number of passenger transport fatalities. Because this city-level model is developed using data sets from different cities all over the world, the impacts of gross domestic product (GDP) are also included in the model.
CONCLUSIONS: Overall, the results imply that the percentage of daily trips by public transport, the percentage of daily trips on foot and by bicycle, and the GDP per inhabitant have negative relationships with the number of passenger transport fatalities, whereas motorway length and the number of motorcycles have positive relationships with the number of passenger transport fatalities.