In-depth characterization of particulate matter in a highly polluted urban environment at the foothills of Himalaya-Karakorum Region.

In recent years, the rising levels of atmospheric particulate matter (PM) have an impact on the earth's system, leading to undesirable consequences on various aspects like human health, visibility, and climate. The present work is carried out over an insufficiently studied but polluted urban area of Peshawar, which lies at the foothills of the famous Himalaya and Karakorum area, Northern Pakistan. The particulate matter with an aerodynamic diameter of less than 10 µm, i.e., PM10 are collected and analyzed for mineralogical, morphological, and chemical properties. Diverse techniques were used to examine the PM10 samples, for instance, Fourier transform infrared spectroscopy, x-ray diffraction, and scanning electron microscopy along with energy-dispersive x-ray spectroscopy, proton-induced x-ray emission, and an OC/EC carbon analyzer. The 24 h average PM10 mass concentration along with standard deviation was investigated to be 586.83 ± 217.70 µg/m3, which was around 13 times greater than the permissible limit of the world health organization (45 µg/m3) and 4 times the Pakistan national environmental quality standards for ambient PM10 (150 µg/m3). Minerals such as crystalline silicate, carbonate, asbestiform minerals, sulfate, and clay minerals were found using FTIR and XRD investigations. Microscopic examination revealed particles of various shapes, including angular, flaky, rod-like, crystalline, irregular, rounded, porous, chain, spherical, and agglomeration structures. This proved that the particles had geogenic, anthropogenic, and biological origins. The average value of organic carbon, elemental carbon, and total carbon is found to be 91.56 ± 43.17, 6.72 ± 1.99, and 102.41 ± 44.90 µg/m3, respectively. Water-soluble ions K+ and OC show a substantial association (R = 0.71). Prominent sources identified using Principle component analysis (PCA) are anthropogenic, crustal, industrial, and electronic combustion. This research paper identified the potential sources of PM10, which are vital for preparing an air quality management plan in the urban environment of Peshawar.

Antimicrobial efficacy of phyto-synthesized silver nanoparticles using aqueous leaves extract of Rosamarinus officinalis L.

The current research investigation demonstrated that the aqueous leaves extract of Rosamarinus officinalis possesses cardinal phyto-chemicals to fabricate AgNPs in an eco-friendly way. The phyto-synthesized AgNPs were characterized to be stable, monodispersed, polycrystalline and mostly spheroidal in conformation. The nano-spheriods were observed to be 25-75 nm in diameter, displaying λmax peak at 430 nm. From the comparative antimicrobial investigations, it was observed that AgNPs manifested tremendous bactericidal properties against all test organisms particularly S. epidermis (89%), S. aureus (84%) and K. pneumonia (84%), owing least MIC values of 40µL. The aced fungicidal activity was also exhibited by AgNPs against all fungal test species particularly C. herbarum (90%), A. flavus (85%), R. stolonifer (85%) and C. jadinii (85%). In contrast to AgNPs, all crude ethanolic, aqueous, methanolic and n-hexanoic extracts manifested less to moderate antimicrobial activity against all test micro-organisms with three-fold escalating MIC values i.e., 160µL.

The effect of COVID-19 lockdown on atmospheric total particle numbers, nanoparticle numbers and mass concentrations in the UK.

The main aim of the COVID-19 lockdown was to curtail the person-to-person transmission of COVID-19. However, it also acted as an air quality intervention. The effect of the lockdown has been extensively analysed on NO2, O3, PM10 and PM2.5, however, little has been done on how total (TPN) and nanoparticle numbers (NPN) have been affected by the lockdown. This paper quantifies the effect of the lockdown on TPN and NPN in the UK, and compares how the effect varies between rural, urban background and traffic sites. Furthermore, the effect on particle numbers is compared with particle mass concentrations, mainly PM10 and PM2.5. Two approaches are used: (a) comparing measured levels of the pollutants in 2019 with 2020 during the lockdown periods; and (b) comparing the predictions of machine learning with measured concentrations using business as usual (BAU) scenario during the lockdown period. P100 (particle size ≤100 nm) increased by 39% at Chilbolton Observatory (CHO) and decreased by 13% and 14% at London Honor Oak Park (LHO) and London Marylebone Road (LMR), respectively. Particles from 101 to 200 nm (P200) showed a similar trend to P100, however, average levels of particles 201-605 nm (P605) decreased at all sites. TPN, PM10 and PM2.5 concentrations decreased at LMR and LHO sites. Estimated PM10, PM2.5 and TPN decreased at all three sites, however, the amount of change varied from site to site. Pollutant concentrations increased back the to pre-pandemic levels, suggesting more sustainable interventions for permanent air quality improvement.

Analysis and Modeling of Air Pollution in Extreme Meteorological Conditions: A Case Study of Jeddah, the Kingdom of Saudi Arabia.

Air pollution has serious environmental and human health-related consequences; however, little work seems to be undertaken to address the harms in Middle Eastern countries, including Saudi Arabia. We installed a continuous air quality monitoring station in Jeddah, Saudi Arabia and monitored several air pollutants and meteorological parameters over a 2-year period (2018-2019). Here, we developed two supervised machine learning models, known as quantile regression models, to analyze the whole distribution of the modeled pollutants, not only the mean values. Two pollutants, namely NO2 and O3, were modeled by dividing their concentrations into several quantiles (0.05, 0.25, 0.50, 0.75, and 0.95) and the effect of several pollutants and meteorological variables was analyzed on each quantile. The effect of the explanatory variables changed at different segments of the distribution of NO2 and O3 concentrations. For instance, for the modeling of O3, the coefficients of wind speed at quantiles 0.05, 0.25, 0.5, 0.75, and 0.95 were 1.40, 2.15, 2.34, 2.31, and 1.56, respectively. Correlation coefficients of 0.91 and 0.92 and RMSE values of 14.41 and 8.96, which are calculated for the cross-validated models of NO2 and O3, showed an acceptable model performance. Quantile analysis aids in better understanding the behavior of air pollution and how it interacts with the influencing factors.

Modelling the Effect of COVID-19 Lockdown on Air Pollution in Makkah Saudi Arabia with a Supervised Machine Learning Approach.

To reduce the spread of COVID-19, lockdowns were implemented in almost every single country in the world including Saudi Arabia. In this paper, the effect of COVID-19 lockdown on O3, NO2, and PM10 in Makkah was analysed using air quality and meteorology data from five sites. Two approaches were employed: (a) comparing raw measured concentrations for the lockdown period in 2019 and 2020; and (b) comparing weather-corrected concentrations estimated by the machine learning approach with observed concentrations during the lockdown period. According to the first approach, the average levels of PM10 and NO2 decreased by 12% and 58.66%, respectively, whereas the levels of O3 increased by 68.67%. According to the second approach, O3 levels increased by 21.96%, while the levels of NO2 and PM10 decreased by 13.40% and 9.66%, respectively. The machine learning approach after removing the effect of changes in weather conditions demonstrated relatively less reductions in the levels of NO2 and PM10 and a smaller increase in the levels of O3. This showed the importance of adjusting air pollutant levels for meteorological conditions. O3 levels increased due to its inverse correlation with NO2, which decreased during the lockdown period.

Source Apportionment of Atmospheric PM10 in Makkah Saudi Arabia by Modelling Its Ion and Trace Element Contents with Positive Matrix Factorization and Generalised Additive Model.

In this paper, the emission sources of PM10 are characterised by analysing its trace elements (TE) and ions contents. PM10 samples were collected for a year (2019−2020) at five sites and analysed. PM10 speciated data were analysed using graphical visualization, correlation analysis, generalised additive model (GAM), and positive matrix factorization (PMF). Annual average PM10 concentrations (µg/m3) were 304.68 ± 155.56 at Aziziyah, 219.59 ± 87.29 at Misfalah, 173.90 ± 103.08 at Abdeyah, 168.81 ± 82.50 at Askan, and 157.60 ± 80.10 at Sanaiyah in Makkah, which exceeded WHO (15 µg/m3), USEPA (50 µg/m3), and the Saudi Arabia national (80 µg/m3) annual air quality standards. A GAM model was developed using PM10 as a response and ions and TEs as predictors. Among the predictors Mg, Ca, Cr, Al, and Pb were highly significant (p < 0.01), Se, Cl, and NO2 were significant (p < 0.05), and PO4 and SO4 were significant (p < 0.1). The model showed R-squared (adj) 0.85 and deviance explained 88.1%. PMF identified four main emission sources of PM10 in Makkah: (1) Road traffic emissions (explained 51% variance); (2) Industrial emissions and mineral dust (explained 27.5% variance); (3) Restaurant and dwelling emissions (explained 13.6% variance); and (4) Fossil fuel combustion (explained 7.9% variance).

Modelling the occurrence and spatial distribution of screwworm species in Northern Pakistan.

We use binomial kriging to model the spatial distribution of myiasis by three species namely Chrysomya bezziana, Wohlfahrtia magnifica and Lucilia cuprina in the livestock of Khyber Pakhtunkhwa, Pakistan. Traditional species distribution models are usually based on assumption of independence of observations. Species data often come in presence-only form for which background points are generated based on some covariates using statistical and machine learning techniques such as MaxEnt. We assume a symmetric binomial distribution based on the principle of maximum entropy in order to decide the number of pseudo-absences. Our results showed that the spatial models fitted very well and prediction distributions were estimated with excellent accuracy. Moreover kriging maps were more accurate as most of the non-spatial variation has been picked up by external drift with higher values of the sensitivity focusing partial AUC for all the three species. Land-use-land-cover was a common factor significantly affecting spatial distribution of all the three species suggesting that for established species anthropogenic factors such as land use become a strong determinant of their spatial distribution. Our results also revealed that for invading species like W. magnifica elevation acts as a barrier to species dispersal and therefore is more limiting to distribution. Furthermore the higher overall prediction accuracy demonstrated that our models performed well in predicting the distributions of the three species, which would lead to better understanding and management of the larval infestation.

Comparing different approaches for assessing the impact of COVID-19 lockdown on urban air quality in Reading, UK.

Many studies investigated the impact of COVID-19 lockdown on urban air quality, but their adopted approaches have varied and there is no consensus as to which approach should be used. In this paper we compare three of the main approaches and assess their performance using both estimated and measured data from several air quality monitoring stations (AQMS) in Reading, Berkshire UK. The approaches are: (1) Sequential approach - comparing pre-lockdown and lockdown periods 2020; (2) Parallel approach - comparing 2019 and 2020 for the equivalent time of the lockdown period; and (3) Machine learning modelling approach - predicting pollution levels for the lockdown period using business as usual (BAU) scenario and comparing with the observations. The parallel and machine learning approaches resulted in relative higher reductions and both showed strong correlation (0.97) and less error with each other. The sequential approach showed less reduction in NO and NOx, showed positive gain in PM10 and NO2 at most of the sites and demonstrated weak correlation with the other two approaches, and is not recommended for such analysis. Overall, the sequential approach showed -14, +4, -32, and + 56% change, the parallel approach showed -46, -43, -43 and + 7% change, and the machine learning approach showed -47, -44, -38 and + 5% change in NOx, NO2, NO and PM10 concentrations, respectively. The pollution roses demonstrated that the UK received easterly polluted winds from the central and eastern Europe, promoting secondary particulates and O3 formation during the lockdown. Changes in pollutant concentrations vary both in space and time according to the approach used, environment type of the monitoring site and the data type (e.g., deweathered vs. raw data). Therefore, the reported results (here or elsewhere) should be viewed in light of these factors before making any conclusion.

Analysing the performance of low-cost air quality sensors, their drivers, relative benefits and calibration in cities-a case study in Sheffield.

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.

Quantifying temporal trends in ground level ozone concentration in the UK.

There is a high interest in quantifying temporal trends in surface ozone concentrations as they serve to quantify the impacts of the anthropogenic precursor reductions and to assess the effects of emission control strategies. In this paper ozone trends for nearly 2 decades (1993 to 2011) at both rural and urban sites have been analysed, using ground level ozone data from 5 urban and 15 rural sites, which are part of the UK AURN. This study analyses ozone trends at various percentiles, in addition to traditional mean trends using quantile regression, TheilSen function, and changepoint analysis. Ozone trends show significant variability at different statistical metrics (e.g., mean, median, maximum and selected quantiles). Maximum trends were negative, whereas median and mean trends were positive during the study period (1993-2011) at both rural and urban sites. Urban and rural trends show different rates of change and indicate that urban decrement (the difference in ozone concentration between rural and urban areas) has been decreasing over the period. Ozone trends were negative during the last 8 years (2004-2011), which could have been caused by the stabilisation of NOx concentration during this period. Furthermore, 3 changepoints were detected in the temporal trend using Pruned Exact Linear Time (PELT) search algorithm, which provides further insight into the ozone temporal trends.