Volatile organic compounds (VOCs) are widely recognized to affect the environment and human health. This review provides a comprehensive presentation of the types and levels of VOCs, their sources and potential effects on human health and the environment based on past and current observations made at tropical sites. Isoprene was found to be the dominant biogenic VOC in the tropics. Tropical broad leaf evergreen trees are the main emitters of isoprene, making up more than 70% of the total emissions. The VOCs found in the tropical remote marine atmosphere included isoprene (>100 ppt), dimethyl sulfide (≤100 ppt) and halocarbons, i.e. bromoform (≤8.4 ppt), dibromomethane (≤2.7 ppt) and dibromochloromethane (≤1.6 ppt). VOCs such as benzene, toluene, ethylbenzene and xylene (BTEX) are the most monitored anthropogenic VOCs and are present mainly due to motor vehicles emissions. Additionally, biomass burning contributes to anthropogenic VOCs, especially high molecular weight VOCs, e.g. methanol and acetonitrile. The relative contributions of VOC species to ozone are determined through the level of the Ozone Formation Potential (OFP) of different species. Emissions of VOCs (e.g. very short-lived halogenated gases) in the tropics are capable of contributing to stratospheric ozone depletion. BTEX has been identified as the main types of VOCs that are associated with the cancer risk in urban areas in tropical regions. Finally, future studies related to VOCs in the tropics and their associated health risks are needed to address these concerns.
Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O₃), nitrogen dioxide (NO₂), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O₃ measurements due to the lack of a reference instrument for CO and NO₂. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO₂) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.
This study aims to determine the composition of BTEX (benzene, toluene, ethylbenzene and xylene) and assess the risk to health at different sites in Malaysia. Continuous monitoring of BTEX in Kuala Lumpur City Centre, Kuala Terengganu, Kota Kinabalu and Fraser Hill were conducted using Online Gas Chromatograph. For comparison, BTEX at selected hotspot locations were determined by active sampling method using sorbent tubes and Thermal Desorption Gas Chromatography Mass Spectrometry. The hazard quotient (HQ) for non-carcinogenic and the life-time cancer risk (LTCR) of BTEX were calculated using the United States Environmental Protection Agency (USEPA) health risk assessment (HRA) methods. The results showed that the highest total BTEX concentrations using continuous monitoring were recorded in the Kuala Lumpur City Centre (49.56 ± 23.71 μg/m3). Toluene was the most dominant among the BTEX compounds. The average concentrations of benzene ranged from 0.69 ± 0.45 μg/m3 to 6.20 ± 3.51 μg/m3. Measurements using active sampling showed that BTEX concentrations dominated at the roadside (193.11 ± 114.57 μg/m3) in comparison to petrol station (73.08 ± 30.41 μg/m3), petrochemical industry (32.10 ± 13.13 μg/m3) and airport (25.30 ± 6.17 μg/m3). Strong correlations among BTEX compounds (p<0.01, r>0.7) at Kuala Lumpur City Centre showed that BTEX compounds originated from similar sources. The values of HQ at all stations were <1 indicating the non-carcinogenic risk are negligible and do not pose threats to human health. The LTCR value based on benzene inhalation (1.59 × 10-5) at Kuala Lumpur City Centre were between 1 × 10-4 and 1 × 10-5, representing a probable carcinogenic risk.
A new 2D titanium carbide (Ti3C2), a low dimensional material of the MXene family has attracted remarkable interest in several electronic applications, but its unique structure and novel properties are still less explored in piezoelectric energy harvesters. Herein, a systematic study has been conducted to examine the role of Ti3C2 multilayers when it is incorporated in the piezoelectric polymer host. The 0.03 g/L of Ti3C2 has been identified as the most appropriate concentration to ensure the optimum performance of the fabricated device with a generated output voltage of about 6.0 V. The probable reasons might be due to the uniformity of nanofiller distribution in the polyvinylidene difluoride (PVDF) and the incorporation of Ti3C2 in a polymer matrix is found to enhance the β-phase of PVDF and diminish the undesired α-phase configuration. Low tapping frequency and force were demonstrated to scavenge electrical energy from abundant mechanical energy resources particularly human motion and environmental stimuli. The fabricated device attained a power density of 14 µW.cm-2 at 10.8 MΩ of load resistor which is considerably high among 2D material-based piezoelectric nanogenerators. The device has also shown stable electrical performance for up to 4 weeks and is practically able to store energy in a capacitor and light up a LED. Hence, the Ti3C2-based piezoelectric nanogenerator suggests the potential to realize the energy harvesting application for low-power electronic devices.
The low-cost and easy-to-use nature of rapidly developed PM2.5 sensors provide an opportunity to bring breakthroughs in PM2.5 research to resource-limited countries in Southeast Asia (SEA). This review provides an evaluation of the currently available literature and identifies research priorities in applying low-cost sensors (LCS) in PM2.5 environmental and health research in SEA. The research priority is an outcome of a series of participatory workshops under the umbrella of the International Global Atmospheric Chemistry Project-Monsoon Asia and Oceania Networking Group (IGAC-MANGO). A literature review and research prioritization are conducted with a transdisciplinary perspective of providing useful scientific evidence in assisting authorities in formulating targeted strategies to reduce severe PM2.5 pollution and health risks in this region. The PM2.5 research gaps that could be filled by LCS application are identified in five categories: source evaluation, especially for the distinctive sources in the SEA countries; hot spot investigation; peak exposure assessment; exposure-health evaluation on acute health impacts; and short-term standards. The affordability of LCS, methodology transferability, international collaboration, and stakeholder engagement are keys to success in such transdisciplinary PM2.5 research. Unique contributions to the international science community and challenges with LCS application in PM2.5 research in SEA are also discussed.
The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include 'on the fly' new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this is presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM.