This paper presents a periodic analysis of a three zone solar pond as a solar energy collector and long term storage system. We explicitly take into account the convective heat and mass flux through the pond surface and evaluate the temperature and heat fluxes at various levels in the pond during its year round operation by solving the time dependent Fourier heat conduction equation with internal heat generation resulting from the absorption of solar radiation in the pond water. Eventually, an expression, for the transient rate at which heat can be retrieved from the solar pond to keep the temperature of the zone of heat extraction as constant, is derived. Heat retrieval efficiencies of 40.0 per cent, 32.1 per cent, 28.3 per cent and 25.5 per cent are predicted at collection temperatures of 40, 60, 80 and 100°C, respectively. the retrieved heat flux exhibits a phase difference of about 30 to 45 days with the incident solar flux; the load levelling in the retrieved heat flux improves as the thickness of the non‐convective zone increases. the efficiency of the solar pond system for conversion of solar energy into mechanical work is also studied. This efficiency is found to increase with collection temperature and it tends to level around 5 per cent at collection temperatures about 90°C.
Low-cost sensors (LCSs) have revolutionized the air pollution monitoring landscape. However, the sensitivities of particulate matter (PM) LCS measurements to various particle microphysical properties and meteorological aspects warrant an accuracy investigation. We investigated the inter-and intracity variations in the accuracy of LCS-measured PM 2.5 across geographically and demographically distinct Indian cities. The collocation data of PM 2.5 (collected during March−April 2022) from an LCS (Atmos) and a reference-grade instrument (β attenuation monitor) from nine sites (across five cities) were analyzed. The root-meansquare error (RMSE) in the hourly mean raw (uncorrected) Atmos PM 2.5 measurements varied significantly across the cities. The Atmos PM 2.5 overestimated the reference-grade PM 2.5 values in cities located in the Indo-Gangetic Plain (Chandigarh and New Delhi) but considerably underestimated the values in the city located in western India (Mumbai). In south Indian cities (Bengaluru and Chennai), the Atmos PM 2.5 measurements were relatively close to the reference-grade PM 2.5 measurements. Among various statistical calibration models trained to correct the Atmos PM 2.5 measurements for most locations, a generalized additive model performed better than other models. The performance of the calibration models was investigated using the holdout cross-validation method. The correction models improved the accuracy of the Atmos PM 2.5 measurements by up to 70%. The bias range of the intracity (Mumbai) raw Atmos PM 2.5 measurements was approximately comparable to the intercity bias range. Across the study locations, the generalized additive model performed the best in correcting the raw LCS PM 2.5 measurements. We also demonstrated that the application of the location-specific calibration model to correct Atmos PM 2.5 measurements improved the accuracy of the LCS PM 2.5 measurements compared with the application of a single-location calibration model for city-wide data.
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