Prediction of Airborne Nanoparticles at Roadside Location Using a Feed–Forward Artificial Neural Network

Accurate prediction of nanoparticles is essential to provide adequate mitigation strategies for air quality management. On the contrary to PM10, SO2, O3, NOx and CO, nanoparticles are not routinely–monitored by environmental agencies as they are not regulated yet. Therefore, a prognostic supervised machine learning technique, namely feed–forward artificial neural network (ANN), has been used with a back–propagation algorithm, to stochastically predict PNCs in three size ranges (N5–30, N30–100 and N100–300 nm). Seven models, covering a total of 525 simulations, were considered using different combinations of the routinely–measured meteorological and five pollutants variables as covariates. Each model included different numbers of hidden layers and neurons per layer in each simulation. Results of simulations were evaluated to achieve the optimum correspondence between the measured and predicted PNCs in each model (namely Models, M1–M7). The best prediction ability was provided by M1 when all the covariate variables were used. The model, M2, provided the lowest prediction performance since all the meteorological variables were omitted in this model. Models, M3–M7, that omitted one pollutant covariate, showed prediction ability similar to M1. The results were within a factor of 2 from the measured values, and provided adequate solutions to PNCs’ prognostic demands. These models are useful, particularly for the studied site where no nanoparticles measurement equipment exist, for determining the levels of particles in various size ranges. The model could be further used for other locations in Kuwait and elsewhere after adequate long–term measurements and training based on the routinely–monitored environmental data

Number and Size Distribution of Airborne Nanoparticles during Summertime in Kuwait: First Observations from the Middle East

We made fast response measurements of size-resolved particle number concentrations (PNCs) and distributions (PNDs) in the 5–1000 nm range close to a busy roadside, continuously for 31 days, in Kuwait. The aims were to understand their dispersion characteristics during summertime and dust events, and association with trace pollutants (NO_<i>x</i>, O₃, CO, SO₂, and PM₁₀) and meteorological parameters. PNCs were found up to ∼19-times higher (5.98 × 10⁵ cm^–3) than those typically found in European roadside environments. Size distributions exhibited over 90% of PNCs in ultrafine size range (<100 nm) and a negligible fraction over 300 nm. Peak PNDs appeared at ∼12 nm, showing an unusually large peak in nucleation mode. Diurnal variations of PNCs coincided with the cyclic variations in CO, NO_<i>x</i>, and traffic volume during morning and evening rush hours. Despite high traffic volume, PNC peaks were missing during noon hours due to high ambient temperature (∼48 °C) that showed an inverse relationship with the PNCs. Principal Component Analysis revealed three probable sources in the arealocal road traffic, fugitive dust, and refineries. Dust events, categorized by PM₁₀ with over 1000 μg m^–3, decreased PNCs by ∼25% and increased their geometric mean diameters (GMDs) by ∼66% compared with nondust periods

Prediction of Airborne Nanoparticles at Roadside Location Using a Feed–Forward Artificial Neural Network

Accurate prediction of nanoparticles is essential to provide adequate mitigation strategies for air quality management. On the contrary to PM10, SO2, O3, NOx and CO, nanoparticles are not routinely–monitored by environmental agencies as they are not regulated yet. Therefore, a prognostic supervised machine learning technique, namely feed–forward artificial neural network (ANN), has been used with a back–propagation algorithm, to stochastically predict PNCs in three size ranges (N5–30, N30–100 and N100–300 nm). Seven models, covering a total of 525 simulations, were considered using different combinations of the routinely–measured meteorological and five pollutants variables as covariates. Each model included different numbers of hidden layers and neurons per layer in each simulation. Results of simulations were evaluated to achieve the optimum correspondence between the measured and predicted PNCs in each model (namely Models, M1–M7). The best prediction ability was provided by M1 when all the covariate variables were used. The model, M2, provided the lowest prediction performance since all the meteorological variables were omitted in this model. Models, M3–M7, that omitted one pollutant covariate, showed prediction ability similar to M1. The results were within a factor of 2 from the measured values, and provided adequate solutions to PNCs’ prognostic demands. These models are useful, particularly for the studied site where no nanoparticles measurement equipment exist, for determining the levels of particles in various size ranges. The model could be further used for other locations in Kuwait and elsewhere after adequate long–term measurements and training based on the routinely–monitored environmental data

Disability Adjusted Life Years (DALYs) in Terms of Years of Life Lost (YLL) Due to Premature Adult Mortalities and Postneonatal Infant Mortalities Attributed to PM_2.5 and PM₁₀ Exposures in Kuwait

Ambient air pollution in terms of fine and coarse particulate matter (PM2.5 and PM10) has been shown to increase adult and infant mortalities. Most studies have estimated the risk of mortalities through attributable proportions and number of excess cases with no reference to the time lost due to premature mortalities. Disability adjusted life years (DALYs) are necessary to measure the health impact of Ambient particulate matter (PM) over time. In this study, we used life-tables for three years (2014&#8315;2016) to estimate the years of life lost (YLL), a main component of DALYs, for adult mortalities (age 30+ years) and postneonatal infant mortalities (age 28+ days&#8315;1 year) associated with PM2.5 exposure and PM10 exposure, respectively. The annual average of PM2.5 and PM10 concentrations were recorded as 87.9 &#956;g/m3 and 167.5 &#956;g/m3, which are 8 times greater than the World Health Organization (WHO) air quality guidelines of 10 &#956;g/m3 and 20 &#956;g/m3, respectively. Results indicated a total of 252.18 (95% CI: 170.69&#8315;322.92) YLL for all ages with an increase of 27,474.61 (95% CI: 18,483.02&#8315;35,370.58) YLL over 10 years. The expected life remaining (ELR) calculations showed that 30- and 65-year-old persons would gain 2.34 years and 1.93 years, respectively if the current PM2.5 exposure levels were reduced to the WHO interim targets (IT-1 = 35 &#956;g/m3). Newborns and 1-year old children may live 79.81 and 78.94 years, respectively with an increase in average life expectancy of 2.65 years if the WHO PM10 interim targets were met (IT-1 = 70 &#956;g/m3). Sensitivity analyses for YLL were carried out for the years 2015, 2025, and 2045 and showed that the years of life would increase significantly for age groups between 30 and 85. Life expectancy, especially for the elderly (&#8805;60 years), would increase at higher rates if PM2.5 levels were reduced further. This study can be helpful for the assessment of poor air quality represented by PM2.5 and PM10 exposures in causing premature adult mortalities and postneonatal infant mortalities in developing countries with high ambient air pollution. Information in this article adds insights to the sustainable development goals (SDG 3.9.1 and 11.6.2) related to the reduction of mortality rates attributed to ambient air levels of coarse and fine particulate matter