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Traditional real-time air quality monitoring instruments are expensive to install and maintain; therefore, such existing air quality monitoring networks are sparsely deployed and lack the measurement density to develop high-resolution spatiotemporal air pollutant maps. More recently, low-cost sensors have been used to collect high-resolution spatial and temporal air pollution data in real-time. In this paper, for the first time, Envirowatch E-MOTEs are employed for air quality monitoring as a case study in Sheffield. Ten E-MOTEs were deployed for a year (October 2016 to September 2017) monitoring several air pollutants (NO, NO2, CO) and meteorological parameters. Their performance was compared to each other and to a reference instrument installed nearby. E-MOTEs were able to successfully capture the temporal variability such as diurnal, weekly and annual cycles in air pollutant concentrations and demonstrated significant similarity with reference instruments. NO2 concentrations showed very strong positive correlation between various sensors. Mostly, correlation coefficients (r values) were greater than 0.92. CO from different sensors also had r values mostly greater than 0.92; however, NO showed r value less than 0.5. Furthermore, several multiple linear regression models (MLRM) and generalised additive models (GAM) were developed to calibrate the E-MOTE data and reproduce NO and NO2 concentrations measured by the reference instruments. GAMs demonstrated significantly better performance than linear models by capturing the non-linear association between the response and explanatory variables. The best GAM developed for reproducing NO2 concentrations returned values of 0.95, 3.91, 0.81, 0.005 and 0.61 for factor of two (FAC2), root mean square error (RMSE), coefficient of determination (R2), normalised mean biased (NMB) and coefficient of efficiency (COE), respectively. The low-cost sensors offer a more affordable alternative for providing real-time high-resolution spatiotemporal air quality and meteorological parameter data with acceptable performance.

The ability to accurately model and predict the ambient concentration of Particulate Matter (PM) is essential for effective air quality management and policies development. Various statistical approaches exist for modelling air pollutant levels. In this paper, several approaches including linear, non-linear, and machine learning methods are evaluated for the prediction of urban PM 10 concentrations in the City of Makkah, Saudi Arabia. The models employed are Multiple Linear Regression Model (MLRM), Quantile Regression Model (QRM), Generalised Additive Model (GAM), and Boosted Regression Trees1-way (BRT1) and 2-way (BRT2). Several meteorological parameters and chemical species measured during 2012 are used as covariates in the models. Various statistical metrics, including the Mean Bias Error (MBE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), the fraction of prediction within a Factor of Two (FACT2), correlation coefficient (R), and Index of Agreement (IA) are calculated to compare the predictive performance of the models. Results show that both MLRM and QRM captured the mean PM 10 levels. However, QRM topped the other models in capturing the variations in PM 10 concentrations. Based on the values of error indices, QRM showed better performance in predicting hourly PM 10 concentrations. Superiority over the other models is explained by the ability of QRM to model the contribution of covariates at different quantiles of the modelled variable (here PM 10 ). In this way QRM provides a better approximation procedure compared to the other modelling approaches, which consider a single central tendency response to a set of independent variables. Numerous recent studies have used these modelling approaches, however this is the first study that compares their performance for predicting PM 10 concentrations.

The size of atmospheric Particulate Matter (PM) is important as a determining factor for how long the particle stays in the atmosphere, and where it deposits in the human respiratory tract. Therefore, it is important to analyse PM 2.5 /PM 10 ratios as an indicator of the fine particles and determine how the ratios vary both in space and time. This study uses the most recent 5 years (2010-2014) PM 2.5 and PM 10 data (µg m -3 ) from 46 monitoring stations, which are part of the UK Automatic Urban and Rural Network (AURN). In this paper mostly robust statistics, which are not sensitive to non-normal distributions and to extreme values in both tails of the distributions are applied to assess temporal trends in PM 2.5 , PM 10 and their ratios. PM 2.5 /PM 10 ratios demonstrated considerable temporal and spatial variability in the UK and 5 years median ranged from 0.4 to 0.8, resulting in overall median of 0.65. Theil-Sen temporal trend analysis showed that PM 2.5 /PM 10 ratios have increased at several monitoring sites in the UK despite the fact that both PM 2.5 and PM 10 levels have predominantly decreased. However, trend in PM 2.5 /PM 10 ratios averaged over the 46 monitoring sites was insignificant. Trends in the ratios of PM 2.5 /PM 10 varied during different seasons: spring showed positive significant trend and winter showed negative significant trend, whereas trends in autumn and summer were insignificant.

Atmospheric Particulate Matter (PM) is considered one of the most critical air pollutants in terms of its detrimental health impacts, environmental degradations and visibility. Particles size, their chemical composition and atmospheric levels are important factors for determining their adverse health impacts. In this paper various aspects of PM 2.5 are analysed including PM 2.5 /PM 10 ratios and association with meteorological parameters using data collected from January 2014 to September 2015 in Makkah Saudi Arabia. During the study period, mean PM 2.5 /PM 10 ratio was found to be 0.64, whereas median and maximum ratios were 0.69 and 0.99, respectively. Diurnal, weekly and annual cycles of PM 10 , PM 2.5 and their ratios were analysed, which demonstrated considerable variations during various hours of the day, days of the week and months of the year. PM 2.5 /PM 10 ratios were lower in summer (June and July) and higher in winter (November and December), likewise the ratios were lower during afternoon and higher in the morning and evening. As expected, there was a positive correlation between PM 10 and PM 2.5 (r = 0.51) and both PM 10 and PM 2.5 showed negative association with relative humidity and positive with wind speed and temperature. Furthermore, PM 2.5 /PM 10 ratios were lower (< 0.45) at lower relative humidity (< 16%) and higher (> 0.70) at higher relative humidity (35-90%), indicating a shift towards high PM 2.5 concentrations at higher relative humidity. Polar plots showed lowest ratios at high wind speed (> 3 m s -1 ) blowing from west and southwest direction in summer, and highest ratios at low wind speed (< 2 m s -1 ) in winter. Polar plots were successfully applied to show the interaction between various meteorological parameters and PM 2.5 /PM 10 ratios. Further work on source apportionment and receptor modelling of PM is required to help develop air quality index and prepare an effective air quality plan for Makkah.

Particulate matter originates from a variety of sources in Makkah, Saudi Arabia. Since Makkah is situated in an arid region and is a very busy city due to its religious importance in the Muslim world, PM 10 concentrations here exceed the international and national air quality standards set for the protection of human health. The main aim of this paper is to model PM 10 concentrations with the aid of meteorological variables (wind speed, wind direction, temperature, and relative humidity) and traffic related air pollutant concentrations (carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO 2 ), sulphur dioxide (SO 2 ) and lag_PM 10 concentrations), which are measured at the same location near Al-Haram (the Holy Mosque) in Makkah. A Generalized Additive Model was developed for predicting hourly PM 10 concentrations. Predicted and observed PM 10 concentrations are compared, and several metrics, including the coefficients of determination (R 2 = 0.52), Root Mean Square Error (RMSE = 84), Fractional Bias (FB = -0.22) and Factor of 2 (FAC2 = 0.88), are calculated to assess the performance of the model. The results of these, along with a graphical comparison of the predicted and observed concentrations, show that model is able to perform well. While effects of all the covariates were significant (p-value < 0.01), the meteorological variables, such as temperature and wind speed, seem to be the major controlling factors with regard to PM 10 concentrations. Traffic related air pollutants showed a weak association with PM 10 concentrations, suggesting road traffic is not the major source of these. No modeling study has been published with regards to air pollution in Makkah and thus this is the first work of this kind. Further work is required to characterize road traffic flow, speed and composition and quantify the contribution of each source, which is part of the ongoing project for managing the air quality in Makkah.

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