Dr Hamid Omidvarborna is a research fellow at the Global Centre for Clean Air Research (GCARE), University of Surrey. Hamid’s research focus falls within air quality, air pollution monitoring, low-cost air pollution sensors, and citizen science activities. Hamid joined GCARE in January 2019 after a 2+ year’s appointment as Research Fellow at Sultan Qaboos University (SQU), Sultanate of Oman (Centre for Environmental Studies and Research (CESAR)), where he worked with Prof Mahad Baawain on different air quality projects in Muscat Governorate and nearby cities. Prior to joining SQU, Hamid spent 4+ years doing research during his PhD in the US under the supervision of Prof Ashok Kumar, where he worked on combustion chemistry of biodiesel fuels, a project supported by the US Department of Transportation (USDOT). Hamid holds a Masters (and a Bachelor) degree in Chemical Engineering from Iran. Following the completion of his Masters and before leaving for the US, Hamid spent 2+ years working as a research and process engineer in two different industries. Details regarding the completed projects and publications can be found in Hamid’s LinkedIn account.
Air Pollution Control
Environmental Monitoring and Assessment
Energy and Environment
We examined the trade-offs between in-car aerosol concentrations, ventilation and respiratory infection transmission under three ventilation settings: windows open (WO); windows closed with air-conditioning on ambient air mode (WC-AA); and windows closed with air-conditioning on recirculation (WC-RC). Forty-five runs, covering a total of 324 km distance on a 7.2-km looped route, were carried out three times a day (morning, afternoon, evening) to monitor aerosols (PM2.5; particulate matter WC-AA>WC-RC) due to the ingress of polluted outdoor air on urban routes. A clear trade-off, therefore, exists for the in-car air quality (icAQ) versus ventilation, where WC-RC showed the least aerosol concentrations (i.e. four-times lower compared with WO), but corresponded to elevated CO2 levels (i.e. five-times higher compared with WO) in 20 mins. We considered COVID-19 as an example of respiratory infection transmission. The probability of its transmission from an infected occupant in a five-seater car was estimated during different quanta generation rates (2-60.5 quanta hr-1) using the Wells-Riley model. In WO, the probability with 50%-efficient and without facemasks under normal speaking (9.4 quanta hr-1) varied only by upto 0.5%. It increased by 2-fold in WC-AA (
We designed a novel experimental set-up to pseudo-simultaneous measure size-segregated filtration efficiency (ηF), breathing resistance (ηP) and potential usage time (tB) for 11 types of face protective equipment (FPE; four respirators; three medical; and four handmade) in the submicron range. As expected, the highest ηF was exhibited by respirators (97±3%), followed by medical (81±7%) and handmade (47±13%). Similarly, the breathing resistance was highest for respirators, followed by medical and handmade FPE. Combined analysis of efficiency and breathing resistance highlighted trade-offs, i.e. respirators showing the best overall performance across these two indicators, followed by medical and handmade FPE. This hierarchy was also confirmed by quality factor, which is a performance indicator of filters. Detailed assessment of size-segregated aerosols, combined with the scanning electron microscope imaging, revealed material characteristics such as pore density, fiber thickness, filter material and number of layers influence their performance. ηF and ηP showed an inverse exponential decay with time. Using their cross-over point, in combination with acceptable breathability, allowed to estimate tB as 3.2-9.5hours (respirators), 2.6-7.3hours (medical masks) and 4.0-8.8hours (handmade). While relatively longer tB of handmade FPE indicate breathing comfort, they are far less efficient in filtering virus-laden submicron aerosols compared with respirators. [Display omitted] •FFP3 respirators showed highest filtration efficiency and breathing resistance.•Multi-layered micro/nano-scale fibres of medical masks offer ηF comparable to respirators.•Highest quality factor was obtained for respirators while the lowest for handmade masks.•FFP3 showed maximum potential usage time and quality factor at acceptable breathability.•SEM images revealed dense aerosol layers deposited on facemasks with thinner fibres.
The emergence of low-cost sensors (LCSs) has rapidly changed the landscape of air pollution monitoring. Unlike regulatory standards with comprehensive processes for performance evaluation and certification for reference equipment, no accreditations or regulatory standards exist for LCSs. Hence, calibration and performance assessment of the LCSs are carried out via co-location experiments with reference instruments under limited ranges of environmental conditions and pollutant concentrations. We designed and tested an environmental-pollution (referred to as ‘Envilution™’) chamber to generate controlled environment for temperature and relative humidity (RH) along with different concentrations of particles so that varied real-world environmental conditions and pollution concentrations can be generated for the performance evaluation of LCSs. The custom-made 125L Envilution™ chamber consists of a humidifier/dehumidifier system, heat pump, particulate matter (PM) generator, a connection for gaseous air pollutants and reference measuring instruments. In the experiments under controlled conditions, the chamber was able to maintain diverse ambient and indoor environmental conditions (temperature range from 5 to 40 °C and RH from 10 to 90%) and stable pollutant concentrations, thereby enabling the use of chamber as a reference environment for LCSs' testing. For demonstration, the assessment was conducted based on temperature/RH (HDC1000 digital) and PM2.5 (HPMA115S0 Honeywell) sensors. A Vaisala HMT120 temperature/RH sensor and optical particle counter (Grimm EDM 107) were employed as reference instruments. The evaluation of LCSs, which were placed inside small enclosure kits, showed excellent correlation for temperature (R2 > 0.96), RH (R2 = 0.99), and PM2.5 (R2 = 0.97) with the reference instruments. The LCSs also demonstrated high linearity agreement (R2 > 0.98) among themselves at temperature (5–35 °C), RH (20–80%), PM2.5 (65–200 μg/m3) measurement ranges. The unique features of the chamber, including affordable cost, small size and lightweight, low maintenance/operational costs and ease of operation, has the potential to make it an on-demand package for LCSs' testing.
The COVID-19 pandemic elicited a global response to limit associated mortality, with social distancing and lockdowns being imposed. In India, human activities were restricted from late March 2020. This ‘anthropogenic emissions switch-off’ presented an opportunity to investigate impacts of COVID-19 mitigation measures on ambient air quality in five Indian cities (Chennai, Delhi, Hyderabad, Kolkata, and Mumbai), using in-situ measurements from 2015 to 2020. For each year, we isolated, analysed and compared fine particulate matter (PM2.5) concentration data from 25 March to 11 May, to elucidate the effects of the lockdown. Like other global cities, we observed substantial reductions in PM2.5 concentrations, from 19 to 43% (Chennai), 41–53 % (Delhi), 26–54 % (Hyderabad), 24–36 % (Kolkata), and 10–39 % (Mumbai). Generally, cities with larger traffic volumes showed greater reductions. Aerosol loading decreased by 29 % (Chennai), 11 % (Delhi), 4% (Kolkata), and 1% (Mumbai) against 2019 data. Health and related economic impact assessments indicated 630 prevented premature deaths during lockdown across all five cities, valued at 0.69 billion USD. Improvements in air quality may be considered a temporary lockdown benefit as revitalising the economy could reverse this trend. Regulatory bodies must closely monitor air quality levels, which currently offer a baseline for future mitigation plans.
The evolution of low-cost sensors (LCSs) has made the spatio-temporal mapping of indoor air quality (IAQ) possible in real-time but the availability of a diverse set of LCSs make their selection challenging. Converting individual sensors into a sensing network requires the knowledge of diverse research disciplines, which we aim to bring together by making IAQ an advanced feature of smart homes. The aim of this review is to discuss the advanced home automation technologies for the monitoring and control of IAQ through networked air pollution LCSs. The key steps that can allow transforming conventional homes into smart homes are sensor selection, deployment strategies, data processing, and development of predictive models. A detailed synthesis of air pollution LCSs allowed us to summarise their advantages and drawbacks for spatio-temporal mapping of IAQ. We concluded that the performance evaluation of LCSs under controlled laboratory conditions prior to deployment is recommended for quality assurance/control (QA/QC), however, routine calibration or implementing statistical techniques during operational times, especially during long-term monitoring, is required for a network of sensors. The deployment height of sensors could vary purposefully as per location and exposure height of the occupants inside home environments for a spatio-temporal mapping. Appropriate data processing tools are needed to handle a huge amount of multivariate data to automate pre-/post-processing tasks, leading to more scalable, reliable and adaptable solutions. The review also showed the potential of using machine learning technique for predicting spatio-temporal IAQ in LCS networked-systems.
The use of cars for drop-off and pick-up of pupils from schools is a potential cause of pollution hotspots at school premises. Employing a joint execution of smart sensing technology and citizen science approach, a primary school took an initiative to co-design a study with local community and researchers to generate data and provide information to understand the impact on pollution levels and identify possible mitigation measures. This study was aimed to assess the hotspots of vehicle-generated particulate matter ≤2.5 μm (PM2.5) and ≤ 10 μm (PM10) at defined drop-off/pick-up points and its ingress into a nearby naturally ventilated primary school classroom. Five different locations were selected inside school premises for measurements during two peak hours: morning (MP; 0730-0930 h; local time) and evening (EP; 1400-1600 h) peak hours, and off-peak (OP; 1100-1300 h) hours for comparison. These represent PM measurements at the main road, pick-up point at the adjoining road, drop-off point, a classroom, and the school playground. Additional measurements of carbon dioxide (CO2) were taken simultaneously inside and outside (drop-off point) the classroom to understand its build-up and ingress of outdoor PM. The results indicate nearly a three-fold increase in the concentrations of fine particles (PM2.5) during drop-off hours compared to off-peak hours indicated the dominant contribution of car queuing in the school premises. Coarse particles (PM2.5–10) were prevalent in the school playground, while the contribution of fine particles as a result of traffic congestion became more pronounced during drop-off hours. In the naturally ventilated classroom, the changes in indoor PM2.5 concentrations during both peak hours (0.58