Outdoor performance of the black globe temperature sensor on a hot and humid tropical region.

A crucial variable to evaluate thermal comfort is the mean radiant temperature (Tmrt). In this paper we evaluate the performance of the 150 mm black globe thermometer to provide reliable Tmrt values for outdoor settings in Singapore. Accurate Tmrt values are calculated by the method of integral radiation measurements. Based on these, the mean convection coefficient of the black globe has been re-calibrated. Results show an improvement in the estimation of Tmrt with the new coefficient in comparison with the default version suggested in ISO7726:1998. Increasing the averaging periods of the measured variables improved the performance of the derived mean convective coefficients to estimate Tmrt. During clear skies day and for 10-min averaged data, RMSE for Tmrt reduce to 3.9 °C (7.4 °C for ISO7726:1998 coefficient) with an overestimation on high incoming solar radiation periods and an underestimation during the morning and evening (low solar elevation). During overcast dry conditions an underestimation of Tmrt is also expected which is higher in the rain/wet periods. The mean convective coefficient presented in this work can provide improved estimations of Tmrt relevant for outdoor thermal comfort studies in hot and humid tropical climates like Singapore.

Air quality impacts of crop residue burning in India and mitigation alternatives.

Crop residue burning contributes to poor air quality and imposes a health burden on India. Despite government bans and other interventions, this practice remains widespread. Here we estimate the impact of changes in agricultural emissions on air quality across India and quantify the potential benefit of district-level actions using an adjoint modeling approach. From 2003 to 2019, we find that agricultural residue burning caused 44,000-98,000 particulate matter exposure-related premature deaths annually, of which Punjab, Haryana, and Uttar Pradesh contribute 67-90%. Due to a combination of relatively high downwind population density, agricultural output, and cultivation of residue-intensive crops, six districts in Punjab alone contribute to 40% of India-wide annual air quality impacts from residue burning. Burning two hours earlier in Punjab alone could avert premature deaths up to 9600 (95% CI: 8000-11,000) each year, valued at 3.2 (95% CI: 0.49-7.3) billion US dollars. Our findings support the use of targeted and potentially low-cost interventions to mitigate crop residue burning in India, pending further research regarding cost-effectiveness and feasibility.

Evaluating the Meteorological Effects on the Urban Form-Air Quality Relationship Using Mobile Monitoring.

Predictive models based on mobile measurements have been increasingly used to understand the spatiotemporal variations of intraurban air quality. However, the effects of meteorological factors, which significantly affect the dispersion of air pollution, on the urban-form-air-quality relationship have not been understood on a granular level. We attempt to fill this gap by developing predictive models of particulate matter (PM) in the Bronx (New York City) using meteorological and urban form parameters. The granular PM data was collected by mobile low-cost sensors as the ground truth. To evaluate the effects of meteorological factors, we compared the performance of models using the urban form within fixed and wind-sensitive buffers, respectively. We find better predictive power in the wind-sensitive group (R = 0.85) for NC10 (number concentration for particles with diameters of 1 µm-10 µm) than the control group (R = 0.01), and modest improvements for PM2.5 (R = 0.84 for the wind sensitive group, R = 0.77 for the control group), indicating that incorporating meteorological factors improved the predictive power of our models. We also found that urban form factors account for 62.95% of feature importance for NC10 and 14.90% for PM2.5 (9.99% and 4.91% for 3-D and 2-D urban form factors, respectively) in our Random Forest models. It suggests the importance of incorporating urban form factors, especially for the uncommonly used 3-D characteristics, in estimating intraurban PM. Our method can be applied in other cities to better capture the influence of urban context on PM levels.

Numerical analysis of the impact of anthropogenic emissions on the urban environment of Singapore.

Increased urbanization and anthropogenic activities in tropical cities lead to the temperature gradient between the urban and rural environments, causing the urban heat island (UHI) phenomenon. This study is a pioneering attempt that examines the changes in the temporal evolution of the surface energy budget induced by urbanization known as the Anthropogenic Influence (AI) in modifying the urban climate of a tropical city using Weather Research and Forecasting (WRF) numerical modeling system. The AI from buildings, traffic and power plants is determined in five different scenarios and the model is validated with high temporal resolution in-situ data. These increased AIs provide improved WRF capability with root mean square error (RMSE) less than 2 °C and mean bias error (MBE) less than 0.5 °C between different performance indicators. Building envelopes (without indoor activity/equipment) are found to be a major contributor in exacerbating the island wide urban heat ∆TaAI, max to 3.7 °C compared to baseline all green scenario. This is followed by the air-conditioner (AC) systems that contribute up to 1.4 °C. The maximum local contribution of traffic and power plants to urban heat is 0.9 °C and 0.4 °C, respectively.

Large global variations in measured airborne metal concentrations driven by anthropogenic sources.

Globally consistent measurements of airborne metal concentrations in fine particulate matter (PM2.5) are important for understanding potential health impacts, prioritizing air pollution mitigation strategies, and enabling global chemical transport model development. PM2.5 filter samples (N ~ 800 from 19 locations) collected from a globally distributed surface particulate matter sampling network (SPARTAN) between January 2013 and April 2019 were analyzed for particulate mass and trace metals content. Metal concentrations exhibited pronounced spatial variation, primarily driven by anthropogenic activities. PM2.5 levels of lead, arsenic, chromium, and zinc were significantly enriched at some locations by factors of 100-3000 compared to crustal concentrations. Levels of metals in PM2.5 and PM10 exceeded health guidelines at multiple sites. For example, Dhaka and Kanpur sites exceeded the US National Ambient Air 3-month Quality Standard for lead (150 ng m-3). Kanpur, Hanoi, Beijing and Dhaka sites had annual mean arsenic concentrations that approached or exceeded the World Health Organization's risk level for arsenic (6.6 ng m-3). The high concentrations of several potentially harmful metals in densely populated cites worldwide motivates expanded measurements and analyses.

Extremely Deformable, Transparent, and High-Performance Gas Sensor Based on Ionic Conductive Hydrogel.

Fabrication of stretchable chemical sensors becomes increasingly attractive for emerging wearable applications in environmental monitoring and health care. Here, for the first time, chemically derived ionic conductive polyacrylamide/carrageenan double-network (DN) hydrogels are exploited to fabricate ultrastretchable and transparent NO2 and NH3 sensors with high sensitivity (78.5 ppm-1) and low theoretical limit of detection (1.2 ppb) in NO2 detection. The hydrogels can withstand various rigorous mechanical deformations, including up to 1200% strain, large-range flexion, and twist. The drastic mechanical deformations do not degrade the gas-sensing performance. A facile solvent replacement strategy is devised to partially replace water with glycerol (Gly) molecules in the solvent of hydrogel, generating the water-Gly binary hydrogel with 1.68 times boosted sensitivity to NO2 and significantly enhanced stability. The DN-Gly NO2 sensor can maintain its sensitivity for as long as 9 months. The high sensitivity is attributed to the abundant oxygenated functional groups in the well-designed polymer chains and solvent. A gas-blocking mechanism is proposed to understand the positive resistance shift of the gas sensors. This work sheds light on utilizing ionic conductive hydrogels as novel channel materials to design highly deformable and sensitive gas sensors.

Highly Stretchable and Transparent Thermistor Based on Self-Healing Double Network Hydrogel.

An ultrastretchable thermistor that combines intrinsic stretchability, thermal sensitivity, transparency, and self-healing capability is fabricated. It is found the polyacrylamide/carrageenan double network (DN) hydrogel is highly sensitive to temperature and therefore can be exploited as a novel channel material for a thermistor. This thermistor can be stretched from 0 to 330% strain with the sensitivity as high as 2.6%/°C at extreme 200% strain. Noticeably, the mechanical, electrical, and thermal sensing properties of the DN hydrogel can be self-healed, analogous to the self-healing capability of human skin. The large mechanical deformations, such as flexion and twist with large angles, do not affect the thermal sensitivity. Good flexibility enables the thermistor to be attached on nonplanar curvilinear surfaces for practical temperature detection. Remarkably, the thermal sensitivity can be improved by introducing mechanical strain, making the sensitivity programmable. This thermistor with tunable sensitivity is advantageous over traditional rigid thermistors that lack flexibility in adjusting their sensitivity. In addition to superior sensitivity and stretchability compared with traditional thermistors, this DN hydrogel-based thermistor provides additional advantages of good transparency and self-healing ability, enabling it to be potentially integrated in soft robots to grasp real world information for guiding their actions.

A 3D Chemically Modified Graphene Hydrogel for Fast, Highly Sensitive, and Selective Gas Sensor.

Reduced graphene oxide (RGO) has proved to be a promising candidate in high-performance gas sensing in ambient conditions. However, trace detection of different kinds of gases with simultaneously high sensitivity and selectivity is challenging. Here, a chemiresistor-type sensor based on 3D sulfonated RGO hydrogel (S-RGOH) is reported, which can detect a variety of important gases with high sensitivity, boosted selectivity, fast response, and good reversibility. The NaHSO3 functionalized RGOH displays remarkable 118.6 and 58.9 times higher responses to NO2 and NH3, respectively, compared with its unmodified RGOH counterpart. In addition, the S-RGOH sensor is highly responsive to volatile organic compounds. More importantly, the characteristic patterns on the linearly fitted response-temperature curves are employed to distinguish various gases for the first time. The temperature of the sensor is elevated rapidly by an imbedded microheater with little power consumption. The 3D S-RGOH is characterized and the sensing mechanisms are proposed. This work gains new insights into boosting the sensitivity of detecting various gases by combining chemical modification and 3D structural engineering of RGO, and improving the selectivity of gas sensing by employing temperature dependent response characteristics of RGO for different gases.

Improved Selectivity and Sensitivity of Gas Sensing Using a 3D Reduced Graphene Oxide Hydrogel with an Integrated Microheater.

Low-cost, one-step, and hydrothermal synthesized 3D reduced graphene oxide hydrogel (RGOH) is exploited to fabricate a high performance NO2 and NH3 sensor with an integrated microheater. The sensor can experimentally detect NO2 and NH3 at low concentrations of 200 ppb and 20 ppm, respectively, at room temperature. In addition to accelerating the signal recovery rate by elevating the local silicon substrate temperature, the microheater is exploited for the first time to improve the selectivity of NO2 sensing. Specifically, the sensor response from NH3 can be effectively suppressed by a locally increased temperature, while the sensitivity of detecting NO2 is not significantly affected. This leads to good discrimination between NO2 and NH3. This strategy paves a new avenue to improve the selectivity of gas sensing by using the microheater to raise substrate temperature.

Improving air quality in high-density cities by understanding the relationship between air pollutant dispersion and urban morphologies.

In high-density megacities, air pollution has a higher impact on public health than cities of lower population density. Apart from higher pollution emissions due to human activities in densely populated street canyons, stagnated air flow due to closely packed tall buildings means lower dispersion potential. The coupled result leads to frequent reports of high air pollution indexes at street-side stations in Hong Kong. High-density urban morphologies need to be carefully designed to lessen the ill effects of high density urban living. This study addresses the knowledge-gap between planning and design principles and air pollution dispersion potentials in high density cities. The air ventilation assessment for projects in high-density Hong Kong is advanced to include air pollutant dispersion issues. The methods in this study are CFD simulation and parametric study. The SST κ-ω model is adopted after balancing the accuracy and computational cost in the comparative study. Urban-scale parametric studies are conducted to clarify the effects of urban permeability and building geometries on air pollution dispersion, for both the outdoor pedestrian environment and the indoor environment in the roadside buildings. Given the finite land resources in high-density cities and the numerous planning and design restrictions for development projects, the effectiveness of mitigation strategies is evaluated to optimize the benefits. A real urban case study is finally conducted to demonstrate that the suggested design principles from the parametric study are feasible in the practical high density urban design.