CHARACTERIZATION AND SOURCE IDENTIFICATION OF POLY CYCLIC AROMATIC HYDROCARBONS (PAHS) FOR COASTAL INDUSTRIAL CITY MANGALORE, INDIA

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental pollutants generated primarily during the incomplete combustion of organic materials. These compounds are contributed to the atmosphere due to various anthropogenic activities in the form of particulate matter. In this study Particulate matter, PM10 samples were collected from a Traffic site (Town hall) and Industrial site (KSPCB) of a coastal city Mangalore in India during post the monsoon period between October to December 2014. The samples were analysed for PAHs namely seven Fluorene (Flu), Acenaphthene (Ace), Chrysene (Chr), Benz(a)anthracene (B(a)A), Benzo(a)pyrene (B(a)P), Benzo(b)fluoranthene (B(b)F), Indeno (1,2,3-c,d) and pyerene (Ind) using fluorescence spectrophotometer. The quarterly average of TPAHs concentration of the industrial site varied from 12 ng/m3 to 109 ng/m3 with an average of about 70.2 ng/m3 whereas TPAHs concentration of traffic site varied from 39 ng/m3 to 252 ng/m3 with an average of 109 ng/m3. Further it was observed that the TPAH concentrations showed increasing trend TPAHoct < TPAHNov < TPAHDec due to meteorological factors. Concurrently TPAH concentrations at traffic site was 1.8 times higher than that of the industrial site. The source apportionment study carried out using Principal Component Analysis (PCA) assisted by varimax rotation revealed that there were only two types of principal components PC1 and PC2. Both the PCs were observed to have variances of 66.21% and 14.38% respectively and classified to originate from fossil fuel burning predominantly diesel/petrol combustion in vehicles for traffic site and the rest from other type of fuels for the industrial site

Classroom ventilation and COVID 19 airborne transmission risk in schools: Insights from COTRACE project

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The impacts of existing and hypothetical green infrastructure scenarios on urban heat island formation

Urban Heat Island (UHI) is posing a significant challenge due to growing urbanisations across the world. Green infrastructure (GI) is popularly used for mitigating the impact of UHI, but knowledge on their optimal use is yet evolving. The UHI effect for large cities have received substantial attention previously. However, the corresponding effect is mostly unknown for towns, where appreciable parts of the population live, in Europe and elsewhere. Therefore, we analysed the possible impact of three vegetation types on UHI under numerous scenarios: baseline/current GI cover (BGI); hypothetical scenario without GI cover (HGI-No); three alternative hypothetical scenarios considering maximum green roofs (HGR-Max), grasslands (HG-Max) and trees (HT-Max) using a dispersion model ADMS-Temperature and Humidity model (ADMS-TH), taking a UK town (Guildford) as a case study area. Differences in an ambient temperature between three different landforms (central urban area, an urban park, and suburban residential area) were also explored. Under all scenarios, the night-time (0200 h; local time) showed a higher temperature increase, up to 1.315 °C due to the lowest atmospheric temperature. The highest average temperature perturbation (change in ambient temperature) was 0.563 °C under HGI-No scenario, followed by HG-Max (0.400 °C), BGI (0.343 °C), HGR-Max (0.326 °C) and HT-Max (0.277 °C). Furthermore, the central urban area experienced a 0.371 °C and 0.401 °C higher ambient temperature compared with its nearby suburban residential area and urban park, respectively. The results allow to conclude that temperature perturbations in urban environments are highly dependent on the type of GI, anthropogenic heat sources (buildings and vehicles) and the percentage of land covered by GI. Among all other forms of GI, trees were the best-suited GI which can play a viable role in reducing the UHI. Green roofs can act as an additional mitigation measure for the reduction of UHI at city scale if large areas are covered

Understanding the effects of roadside hedges on the horizontal and vertical distributions of air pollutants in street canyons

Built-up environments limit air pollution dispersion in street canyons and lead to complex trade-offs between green infrastructure (GI) usage and its potential to reduce near-road exposure. This study evaluated the effects of an evergreen hedge on the distribution of particulate matter (PM1, PM2.5, PM10), black carbon (BC) and particle number concentrations (PNCs) in a street canyon in West London. Instrumentation was deployed around the hedge at 13 fixed locations to assess the impact of the hedge on vertical and horizontal concentration distributions. Changes in concentrations behind the hedge were measured with reference to the corresponding sampling point in front of the hedge for all sets of measurements. Results showed a significant reduction in vertical concentrations between 1 and 1.7 m height, with maximum reductions of –16% (PM1 and PM10) and –17% (PM2.5) at ∼1 m height. Horizontal concentrations revealed two zones between the building façade and the hedge, with opposite trends: (i) close to hedge (within 0.2 m), where a reduction of PM1 and PM2.5 was observed, possibly due to dilution, deposition and the barrier effect; and (ii) 0.2–3 m from the hedge, showing an increase of 13–37% (PM1) and 7–21% (PM2.5), possibly due to the blockage effect of the building, restricting dispersion. BC showed a significant reduction at breathing height (1.5 m) of between –7 and –50%, followed by –15% for PNCs in the 0.02–1 µm size range. The ELPI + analyser showed a peak of ∼30 nm. The presence of the hedge led to a ∼39 ± 32% decrease in total PNCs (0.006–10 µm), suggesting a greater removal in different modes, such as a 83 ± 12% reduction in nucleation mode (0.006–0.030 µm), 74 ± 15% in ultrafine (≤0.1 µm), and 34 ± 30% in accumulation mode (0.03–0.3 µm). These findings indicate graded filtering of particles by GI in a near-road street canyon environment. This insight will guide the improved design of GI barriers and the validation of microscale dispersion models

Pollutant concentrations and exposure variability in four urban microenvironments of London

We compared various pollutant concentrations (PM1, PM2.5, PM10, PNC, BC) at four different urban microenvironments (MEs) in London (Indoor, IN; Traffic Intersection, TI; Park, PK; and Street Canyon, SC). The physico-chemical characteristics of particles were analysed, and the respiratory deposition doses (RDD) were estimated. Field measurements were conducted over a period of 121 days. The mean PM2.5 (PNC) concentrations were found to be 9.47 ± 7.05 (16366 ± 11815), 8.09 ± 4.57 (10951 ± 6445), 5.11 ± 2.96 (7717 ± 4576), 3.88 ± 3.06 (5672 ± 2934) μg m−3 (# cm−3) at TI, SC, PK and IN, respectively. PM2.5, PM10 and PNC exhibited a trend of TI \u3e SC \u3e PK \u3e IN; higher concentrations for PM1 and BC were observed at IN than PK due to the emissions from printers, producing a trend of TI \u3e SC \u3e IN \u3e PK. We observed 12%–30% higher fine PM concentrations at TI and SC sites during morning peak (07:00–09:30) than the evening peak hours (16:00–19:00); while IN showed a smaller variation in fine PM concentrations compared with outdoor TI, PK and SC sites owing to their prevalence in the IN for a longer time. Fine and ultrafine PM containing potentially toxic trace transition metals including Fe, Ti, Cr, Mn, Al and Mg were detected by high resolution electron microscopy at all sites. There was a similar relative abundance of different elements at the TI, IN and PK sites, which suggests a transport of PM between MEs. RDD for PM1 was highest (2.45 ± 2.27 μg h−1) at TI for females during running; PM2.5 and PM10 were highest at SC (11.23 ± 6.34 and 37.17 ± 20.82 μg h−1, respectively). The results show that the RDD variation between MEs does not follow the PM concentration trend. RDD at PK was found to be 39%–53% lower than TI and SC during running for all the PM fractions. Overall, the study findings show the air quality variation at different MEs and reveals the exposure inequalities around the city, which enable the management of personal exposure by selecting appropriate MEs for different activities

Corrigendum to “Pollutant concentrations and exposure variability in four urban microenvironments of London” [Atmos. Environ. 298 (2023) 119624](S135223102300050X)(10.1016/j.atmosenv.2023.119624)

The authors regret to inform the missing name from the co-authors list and apologise for any inconvenience caused. The new authors’ list should read as: Mamatha Tomsona,e, Prashant Kumara,b,c,d, Gopinath Kalaiarasana, Juan C. Zavala-Reyesa, f, Marta Chiapascoh, Mark A. Sephtong, Gloria Youngh, Alexandra E. Porterh, Michał M. KłosowskiiaGlobal Centre for Clean Air Research (GCARE), School of Sustainability, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH, Surrey, United Kingdom bInstitute for Sustainability, University of Surrey, Guildford, GU2 7XH, Surrey, United Kingdom cDepartment of Civil, Structural & Environmental Engineering, Trinity College Dublin, Dublin, Ireland dSchool of Architecture, Southeast University, Nanjing, China eSMART Infrastructure Facility, Faculty of Engineering and Information Science, University of Wollongong, Wollongong, 2522, NSW, Australia fEscuela Nacional de Estudios Superiores–Mérida, Universidad Nacional Autónoma de México, 97357, Mérida, Yucatán, México gDepartment of Earth Science and Engineering, Prince Consort Road, Imperial College London, SW72AZ, United Kingdom hDepartment of Materials, Prince Consort Road, Imperial College London, SW72AZ, United Kingdom iResearch Complex at Harwell, Harwell Campus R92, OX11 0FA, United Kingdom The updated CRediT authorship contribution statement should read as: Mamatha Tomson: Conceptualization, Writing – original draft, Methodology, Validation, Visualization, Writing – review & editing. Prashant Kumar: Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review & editing. Gopinath Kalaiarasan: Writing – review & editing. Juan C. Zavala-Reyes: Writing – review & editing. Marta Chiapasco: Writing – review & editing. Mark A. Sephton: Writing – review & editing. Gloria Young: Writing – review & editing. Alexandra E. Porter: Writing – review & editing, Funding acquisition. Michał M. Kłosowski: Investigation and Formal analysis (Section 3.5)