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Item A Landsat-derived annual inland water clarity dataset of China between 1984 and 2018(Copernicus, 2022-01-13) Tao, Hui; Song, Kaishan; Liu, Ge; Wang, Qiang; Wen, Zhidan; Jacinthe, Pierre-Andre; Xu, Xiaofeng; Du, Jia; Shang, Yingxin; Li, Sijia; Wang, Zongming; Lyu, Lili; Hou, Junbin; Wang, Xiang; Liu, Dong; Shi, Kun; Zhang, Baohua; Duan, Hongtao; Earth and Environmental Sciences, School of ScienceWater clarity serves as a sensitive tool for understanding the spatial pattern and historical trend in lakes' trophic status. Despite the wide availability of remotely sensed data, this metric has not been fully explored for long-term environmental monitoring. To this end, we utilized Landsat top-of-atmosphere reflectance products within Google Earth Engine in the period 1984–2018 to retrieve the average Secchi disk depth (SDD) for each lake in each year. Three SDD datasets were used for model calibration and validation from different field campaigns mainly conducted during 2004–2018. The red blue band ratio algorithm was applied to map SDD for lakes (>0.01 km2) based on the first SDD dataset, where R2=0.79 and relative RMSE (rRMSE) =61.9 %. The other two datasets were used to validate the temporal transferability of the SDD estimation model, which confirmed the stable performance of the model. The spatiotemporal dynamics of SDD were analyzed at the five lake regions and individual lake scales, and the average, changing trend, lake number and area, and spatial distribution of lake SDDs across China were presented. In 2018, we found the number of lakes with SDD <2 m accounted for the largest proportion (80.93 %) of the total lakes, but the total areas of lakes with SDD of <0.5 and >4 m were the largest, both accounting for about 24.00 % of the total lakes. During 1984–2018, lakes in the Tibetan–Qinghai Plateau region (TQR) had the clearest water with an average value of 3.32±0.38 m, while that in the northeastern region (NLR) exhibited the lowest SDD (mean 0.60±0.09 m). Among the 10 814 lakes with SDD results for more than 10 years, 55.42 % and 3.49 % of lakes experienced significant increasing and decreasing trends, respectively. At the five lake regions, except for the Inner Mongolia–Xinjiang region (MXR), more than half of the total lakes in every other region exhibited significant increasing trends. In the eastern region (ELR), NLR and Yungui Plateau region (YGR), almost more than 50 % of the lakes that displayed increase or decrease in SDD were mainly distributed in the area range of 0.01–1 km2, whereas those in the TQR and MXR were primarily concentrated in large lakes (>10 km2). Spatially, lakes located in the plateau regions generally exhibited higher SDD than those situated in the flat plain regions. The dataset is freely available at the National Tibetan Plateau Data Center (https://doi.org/10.11888/Hydro.tpdc.271571, Tao et al., 2021).Item Estimated validity and reliability of on-board diagnostics for older vehicles: comparison with remote sensing observations(2011-09) Supnithadnaporn, Anupit; Noonan, Douglas S.; Samoylov, Alexander; Rodgers, Michael O.Based on requirements under the Clean Air Act Amendments of 1990, most state vehicle inspection and maintenance (I/M) programs have, since 2002, replaced the tailpipe emission testing with the on-board diagnostic (OBD) II testing for 1996 model and newer vehicles. This test relies on the OBD II system to give the pass or fail result, depending on certain conditions that might cause the vehicle to emit pollution 1.5 times higher than the regulated standard. The OBD II system is a computer and sensors installed in the vehicle to monitor the emission control units and signal if there is any malfunction. As a vehicle ages, its engine, pollution control units, and OBD II system deteriorate. Because the OBD II system's durability directly influences the test outcome, it is important to examine the fleetwide trend in the OBD II test results in comparison with an alternative measure of identifying high emitting vehicles. This study investigates whether the validity and reliability of the OBD II test is related to the age of the OBD II system installed in the fleet. Using Atlanta's I/M testing records and remote sensing device (RSD) data collected during 2002-2005, this research establishes the convergent validity and interobserver reliability criteria for the OBD II test based on on-road emissions measured by RSDs. The study results show that older vehicles exhibit significantly lower RSD-OBD II outcome agreement than newer vehicles. This suggests that the validity and reliability of the OBD II test may decline in the older vehicle fleets. Explanations and possible confounding factors for these findings are discussed.Item Towards Long-Term Multi-Hop WSN Deployments for Environmental Monitoring: An Experimental Network Evaluation(MDPI, 2014-12-05) Navarro, Miguel; Davis, Tyler W.; Villalba, German; Li, Yimei; Zhong, Xiaoyang; Erratt, Newlyn; Liang, Xu; Liang, Yao; Computer and Information Science, School of ScienceThis paper explores the network performance and costs associated with the deployment, labor, and maintenance of a long-term outdoor multi-hop wireless sensor network (WSN) located at the Audubon Society of Western Pennsylvania (ASWP), which has been in operation for more than four years for environmental data collection. The WSN performance is studied over selected time periods during the network deployment time, based on two different TinyOS-based WSN routing protocols: commercial XMesh and the open-source Collection Tree Protocol (CTP). Empirical results show that the network performance is improved with CTP (i.e., 79% packet reception rate, 96% packet success rate and 0.2% duplicate packets), versus using XMesh (i.e., 36% packet reception rate and 46% packet success rate, with 3%–4% duplicate packets). The deployment cost of the 52-node, 253-sensor WSN is $31,500 with an additional $600 per month in labor and maintenance resulting in a cost of $184 m−2·y−1 of sensed area. Network maintenance during the first four years of operation was performed on average every 12 days, costing approximately $187 for each field visit.