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Jay Dhariwal

Publications and source records attributed to Jay Dhariwal.

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Design and Development of Low-Cost Datalogger for Indoor and Outdoor Air Quality Monitoring

The rising demand for low-cost air quality monitors stems from increased public awareness and interest within the research community. These monitors play a pivotal role in empowering citizens and scientists to comprehend spatiotemporal variations in air quality parameters, aiding in the formulation of effective mitigation policies. The primary challenge lies in the diverse array of application scenarios these monitors encounter. The developed data logging device is exceptionally well-suited for air quality monitoring applications, offering exceptional versatility by seamlessly operating on a range of power sources, including solar energy, batteries, and direct electrical supply. The integration of a built-in battery charger enhances its applicability for deployment in regions with solar power or intermittent electricity availability. To ensure strong network connectivity, the advanced datalogger seamlessly integrates with WiFi, Bluetooth, and LoRaWAN networks. A notable feature is its adaptable MCU system, enabling users to swap the MCU based on specific connectivity, power, and computational requirements. Importantly, the system carefully identifies key parameters crucial for both indoor and outdoor air quality assessment, customizing sensor selection accordingly. Furthermore, optimization efforts have prioritized energy efficiency, enabling the system to function with minimal power consumption while maintaining data integrity. Additional I2C and UART ports facilitate the monitoring of supplementary parameters.

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Reliability Assessment of Low-Cost PM Sensors under High Humidity and High PM Level Outdoor Conditions

Low-cost particulate matter (PM) sensors have become increasingly popular due to their compact size, low power consumption, and cost-effective installation and maintenance. While several studies have explored the effects of meteorological conditions and pollution exposure on low-cost sensor (LCS) performance, few have addressed the combined impact of high PM concentration and high humidity levels. In contrast to most evaluation studies, which generally report $\text{PM}_{2.5}$ levels below $150~\mu\text{g/m}^3$, our study observed hourly average $\text{PM}_{2.5}$ concentrations ranging from $6-611~\mu\text{g/m}^3$ (mean value of $137~\mu\text{g/m}^3$), with relative humidity between $25-95\%$ (mean value of $72\%$), and temperature varying from $6-29^\circ$C (mean value of $16^\circ$C). We evaluate three LCS models (SPS30, PMS7003, HPMA115C0-004) in outdoor conditions during the winter season in New Delhi, India, deployed alongside a reference-grade beta attenuation monitor (BAM). The results indicate a strong correlation between LCS and BAM measurements (${R^2} > 90\%$). The RMSE increases with increasing PM concentration and humidity levels but the narrow $95\%$ confidence interval range of LCS as a function of the reference BAM suggests the importance of LCS in air pollution monitoring. Among the evaluated LCS models, SPS30 showed the highest overall accuracy. Overall, the study demonstrates that LCS can effectively monitor air quality in regions with high PM and high humidity levels, provided appropriate correction models are applied.

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