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The wellbore skin effect on slug‐test results was analyzed using numerical simulation and field tests for a well at progressive stages of development. The numerical simulation is based on a composite flow model that incorporates a zone of disturbed formation surrounding the wellbore. Field tests were performed on a water‐bearing clayey silt formation at a ground‐water remediation site in Wisconsin. Based on the numerical simulation, the radius of investigation was examined. The results show that the early‐time and late‐time data reflect ground‐water flow in the wellbore skin and undisturbed formation, respectively. Both the numerically simulated and the field slug‐test data define a downward concave curve on a semilog plot of time versus the logarithm of dimensionless head. For the Hvorslev (1951) and Bouwer and Rice (1976) methods, the late‐time segment of the simulated data yields estimates of hydraulic conductivity close to the value defined in the flow model. When a wellbore skin exists, the data curve in a plot of the logarithm of time versus the dimensionless head is shifted horizontally along the time axis. This shift leads to an inaccurate determination of hydraulic conductivity based on the Cooper et al. (1967) method. In the plots of time versus dimensionless head derivatives, the data curve geometry depends on the hydraulic properties of the wellbore skin. Consequently, the wellbore skin effect can be identified and eliminated using derivative‐based type curve methods. For low‐permeability materials, the effect of wellbore skin on estimates of hydraulic conductivity can be minimized through use of the late‐time data. However, proper well installation and development appears to be the most effective and practical solution.

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Heat stress caused by high air temperature and high humidity is a serious health concern for urban residents. Mobile measurement of these two parameters can complement weather station observations because of its ability to capture data at fine spatial scales and in places where people live and work. In this paper, we describe a smart temperature and humidity sensor (Smart-T) for use on bicycles to characterize intracity variations in human thermal conditions. The sensor has several key characteristics of IoT (Internet of Things) technology, including light-weight, low-cost, low power consumption, ability to communicate and geo-locate the data (via the cyclist’s smartphone), and potential to be deployed in large quantities. The sensor has a reproducibility of 0.03 to 0.05°C for temperature and of 0.18 to 0.33 % for relative humidity (one standard deviation of variation among multiple units). The time constant with a complete radiation shelter and moving at a normal cycling speed is 9.7 s and 18.5 s for temperature and humidity, respectively, corresponding to a spatial resolution of 40 m and 70 m. Measurements were made with the sensor on street transects in Nanjing, China. Results show that increasing vegetation fraction causes reduction in both air temperature and absolute humidity and that increasing impervious surface fraction has the opposite effect.

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