Sairam Dhandaphani

Air pollutants are a major by-product of urbanisation and motorisation of society. In lower and upper middle-income Asian countries, in cities with rapid population growth such as Malaysia, traffic emissions are responsible for almost 90% of urban air pollution, so cycling or walking outdoor can be a major route of exposure for active commuters. Our study aims to examine the association between traffic-related air pollution and cardiorespiratory health symptoms among pedestrian and cyclists in a university campus located in Selangor, Malaysia.  PM2.5 concentrations were monitored using SidePak Personal Aerosol Monitor AM510 on weekday morning cycling and walking commutes into designated high and low-traffic areas nearby campus roadsides. Volunteers cycled (n=21) and walked (n=30) for about 60-minutes in high and low-traffic cycling and walking routes respectively. The cardiorespiratory health status of blood pressure and lung function were measured before, immediately after, after 15 minutes and after 1 hour of volunteers’ commutes. The average commute exposure to PM2.5 was determined, and the inhaled dosage was estimated. Results showed that pedestrian are exposed to higher PM2.5 levels than cyclists traveling in the same high-traffic areas. However, the inhalation dose per kilometre travelled, DL (µg/km) for cyclist was observed higher compared to the pedestrian due to the ventilation rate of physical activity. We also observed that there were increase in the systolic blood pressure and lung function (force-vital capacity-FVC) of pedestrians after the exposure to high PM2.5 concentrations at high traffic walking routes (61.6 ± 14.6 µg/m³). PM2.5 concentrations while walking in the university campus were approximately three times higher compared to cities in Europe (26 μg m−3). Our observation techniques can be applied in resource-constrained countries with heavy traffic emissions that may have an impact on the health of active commuters. To characterise the exposure patterns of other traffic-related air pollutant surrogates (such as soot/black carbon and nitrogen dioxide) and their influence on acute and chronic health outcomes in different Asian traffic microenvironments, further research based on the results of our study is needed.

Black carbon (BC) is of concern due to its contribution to poor air quality and its adverse effects human health. We carried out the first real-time monitoring of BC in Malaysia using an AE33 Aethalometer. Measurements were conducted between 1 January and 31 May 2020 in a university area in a suburban location of the Klang Valley. The measurement period coincided with the implementation of a movement control order (MCO) in response to COVID-19. The mean concentration of BC before the MCO was 2.34 µg/m³ which decreased by 38% to 1.45 µg/m³ during the MCO. The BC is dominated by fossil-fuel sources (mean proportion BC_ff = 79%). During the MCO, the BC_ff concentration decreased by more than the BC_bb concentration derived from biomass burning. BC and BC_ff show very strong diurnal cycles, which also show some weekday–weekend differences, with maxima during the night and just before noon, and minima in the afternoon. These patterns indicate strong influences on concentrations from both traffic emissions and boundary layer depth. BC was strongly correlated with NO₂ (R = 0.71), another marker of traffic emission, but less strongly with PM_2.5 (R = 0.52). The BC absorption Ångström exponent (AAE) ranged between 1.1 and 1.6. We observed pronounced diurnal cycles of lower AAE in daytime, corresponding to BC_ff contributions from traffic. Average AAE also showed a pronounced increase during the MCO. Our data provides a new reference for BC in suburban Malaysia for the public and policy-makers, and a baseline for future measurements.

The proposed work focuses on the design and development of an embedded system framework to test the suitability of the soil for urban terrace farming. The need to rush to an agricultural officer or department can be reduced to a certain extent in testing the quality and type of soil at regular intervals with this system. This helps the user in deciding the best plant (specifically cash crops) and fertilizers that are to be used for the growth of plants in the given soil conditions. The soil conditions can be estimated by measuring the pH and moisture content in the soil. The concentration of hydrogen ions (pH) and moisture sensors are used to estimate the pH and soil moisture levels for a particular cash crop. The idea behind the implementation of the proposed system using FPGA is to design it as an Application Specific Integrated Circuit (ASIC) to ensure the features of parallel processing computations, field re-programmability and faster time-to-market, low cost and power consumption. The proposed user-friendly system eases the process in checking the suitability of the soil, predicting the plants that can be grown in any given condition. The developed framework has been calibrated with the help of a standard pH meter to satisfy the intended use.