Department of Biology, University of North Texas, Denton, Texas, United States of America.
Department of Entomology, Texas A&M University, College Station, Texas, United States of America.
PLoS One. 2020 Jan 29;15(1):e0227540. doi: 10.1371/journal.pone.0227540. eCollection 2020.
An increasing number of citizen science water monitoring programs is continuously collecting water quality data on streams throughout the United States. Operating under quality assurance protocols, this type of monitoring data can be extremely valuable for scientists and professional agencies, but in some cases has been of limited use due to concerns about the accuracy of data collected by volunteers. Although a growing body of studies attempts to address accuracy concerns by comparing volunteer data to professional data, rarely has this been conducted with large-scale datasets generated by citizen scientists. This study assesses the relative accuracy of volunteer water quality data collected by the Texas Stream Team (TST) citizen science program from 1992-2016 across the State of Texas by comparing it to professional data from corresponding stations during the same time period. Use of existing data meant that sampling times and protocols were not controlled for, thus professional and volunteer comparisons were refined to samples collected at stations within 60 meters of one another and during the same year. Results from the statewide TST dataset include 82 separate station/year ANOVAs and demonstrate that large-scale, existing volunteer and professional data with unpaired samples can show agreement of ~80% for all analyzed parameters (DO = 77%, pH = 79%, conductivity = 85%). In addition, to assess whether limiting variation within the source datasets increased the level of agreement between volunteers and professionals, data were analyzed at a local scale. Data from a single partner city, with increased controls on sampling times and locations and correction of a systematic bias in DO, confirmed this by showing an even greater agreement of 91% overall from 2009-2017 (DO = 91%, pH = 83%, conductivity = 100%). An experimental sampling dataset was analyzed and yielded similar results, indicating that existing datasets can be as accurate as experimental datasets designed with researcher supervision. Our findings underscore the reliability of large-scale citizen science monitoring datasets already in existence, and their potential value to scientific research and water management programs.
越来越多的公民科学水质监测项目持续在美国各地的溪流中收集水质数据。在质量保证协议的规定下，这种监测数据对科学家和专业机构非常有价值，但在某些情况下，由于对志愿者收集的数据的准确性存在担忧，其用途有限。尽管越来越多的研究试图通过将志愿者数据与专业数据进行比较来解决准确性问题，但很少有研究是针对公民科学家产生的大规模数据集进行的。本研究通过将德克萨斯州溪流队（TST）公民科学项目从 1992 年至 2016 年在整个德克萨斯州收集的志愿者水质数据与同期相应站点的专业数据进行比较，评估了志愿者水质数据的相对准确性。由于使用了现有数据，因此无法控制采样时间和方案，因此对专业人员和志愿者的比较进行了细化，只比较了在彼此相距 60 米的站点收集的样本，以及同年收集的样本。全州范围的 TST 数据集的结果包括 82 个单独的站点/年方差分析，结果表明，大规模的现有志愿者和专业数据与非配对样本之间可以达到约 80%的所有分析参数的一致性（溶解氧=77%，pH 值=79%，电导率=85%）。此外，为了评估在源数据集内限制变异性是否会增加志愿者和专业人员之间的一致性，在局部尺度上分析了数据。从一个单一的合作城市获得的数据，对采样时间和地点的控制增加，以及对溶解氧的系统偏差进行了修正，从 2009 年至 2017 年的数据中证实了这一点，总体一致性更高，达到 91%（溶解氧=91%，pH 值=83%，电导率=100%）。对一个实验性采样数据集进行了分析，结果也得到了类似的结果，表明现有的数据集可以像具有研究人员监督的实验性数据集一样准确。我们的研究结果强调了已有的大规模公民科学监测数据集的可靠性，以及它们对科学研究和水资源管理计划的潜在价值。
