Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state‐of‐the‐art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing‐based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water and natural hazard management are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined

A framework for designing and optimizing green infrastructure network under uncertainty

Green infrastructure (GI) is becoming a common solution to mitigate stormwater-related problems. Despite wide acknowledgement of GI benefits, there is a lack of decision support tools that allow practitioners to interactively identify and evaluate the performance of small GI practices using hydrologic models under uncertainty. Also, the benefits and costs of GI practices are not fully understood when the analysis scale changes from a household to a subwatershed to an entire watershed. Moreover, recognition of optimal locations in a watershed, given the uncertainty in modelling parameters, is also another challenge for GI planning and design. To address these needs, an online Cloud-based interactive tool — called Interactive DEsign and Assessment System for Green Infrastructure (IDEAS_GI)— has been developed. This study demonstrates the application of the tool, using hydrologic and empirical models, to estimate life cycle cost, stormwater volume reduction and treatment, and air pollutant deposition. The tool was applied in two small watersheds in the Baltimore metropolitan area. The results show that GI properties do not significantly affect performance of individual GI practices during design storm events due to the intensity of the storms exceeding the capacity of GI practices to treat and capture stormwater. Using the tool to identify potential locations for GI placement, the study then provides a quantitative and comparative analysis of environmental benefits and economic costs of GI using two metrics [Benefit-Cost Ratios (BCRs) and nutrient removal costs] at household, subwatershed, and watershed scales. The results for a case study in Baltimore show that the unit cost of nutrient removal in some of the subwatersheds is lower than the unit costs at either the watershed or household scales, calling for optimization frameworks to determine the features that dictate optimality at the subwatershed level. Moreover, rain gardens provide far more efficient stormwater treatment at the household scale in comparison to watershed scale, for which large-scale dry or wet basins are more efficient. The results show that for BCR, smaller subwatersheds are more cost effective for GI implementation, while for nutrient removal cost, upstream subwatersheds are more suitable. Furthermore, self-installation of rain gardens greatly reduces nutrient removal costs. Finally, to identify preferable locations for GI implementation, the numerical hydrologic model used in IDEAS_GI, SWMM, has been merged with a probabilistic noisy genetic algorithm (GA). The GA uses a probabilistic selection method that requires numerous sampling realizations to estimate the uncertainties associated with the fitness (objective function) values, which are cumulative stormwater volume reduction and GI life cycle cost. To overcome the computational challenge and to identify significant features for preferable locations, the GA is merged with artificial neural networks, which act as surrogates for the numerical models. The surrogate models use GA-generated archives as training datasets to predict the mean and standard deviation of cumulative stormwater volume reduction. The results show that the addition of meta-models decreases average computational time required to reach Pareto frontiers similar to the ones generated by the noisy GA by more than 95%

A framework for designing and optimizing green infrastructure network under uncertainty

Green infrastructure (GI) is becoming a common solution to mitigate stormwater-related problems. Despite wide acknowledgement of GI benefits, there is a lack of decision support tools that allow practitioners to interactively identify and evaluate the performance of small GI practices using hydrologic models under uncertainty. Also, the benefits and costs of GI practices are not fully understood when the analysis scale changes from a household to a subwatershed to an entire watershed. Moreover, recognition of optimal locations in a watershed, given the uncertainty in modelling parameters, is also another challenge for GI planning and design. To address these needs, an online Cloud-based interactive tool — called Interactive DEsign and Assessment System for Green Infrastructure (IDEAS_GI)— has been developed. This study demonstrates the application of the tool, using hydrologic and empirical models, to estimate life cycle cost, stormwater volume reduction and treatment, and air pollutant deposition. The tool was applied in two small watersheds in the Baltimore metropolitan area. The results show that GI properties do not significantly affect performance of individual GI practices during design storm events due to the intensity of the storms exceeding the capacity of GI practices to treat and capture stormwater. Using the tool to identify potential locations for GI placement, the study then provides a quantitative and comparative analysis of environmental benefits and economic costs of GI using two metrics [Benefit-Cost Ratios (BCRs) and nutrient removal costs] at household, subwatershed, and watershed scales. The results for a case study in Baltimore show that the unit cost of nutrient removal in some of the subwatersheds is lower than the unit costs at either the watershed or household scales, calling for optimization frameworks to determine the features that dictate optimality at the subwatershed level. Moreover, rain gardens provide far more efficient stormwater treatment at the household scale in comparison to watershed scale, for which large-scale dry or wet basins are more efficient. The results show that for BCR, smaller subwatersheds are more cost effective for GI implementation, while for nutrient removal cost, upstream subwatersheds are more suitable. Furthermore, self-installation of rain gardens greatly reduces nutrient removal costs. Finally, to identify preferable locations for GI implementation, the numerical hydrologic model used in IDEAS_GI, SWMM, has been merged with a probabilistic noisy genetic algorithm (GA). The GA uses a probabilistic selection method that requires numerous sampling realizations to estimate the uncertainties associated with the fitness (objective function) values, which are cumulative stormwater volume reduction and GI life cycle cost. To overcome the computational challenge and to identify significant features for preferable locations, the GA is merged with artificial neural networks, which act as surrogates for the numerical models. The surrogate models use GA-generated archives as training datasets to predict the mean and standard deviation of cumulative stormwater volume reduction. The results show that the addition of meta-models decreases average computational time required to reach Pareto frontiers similar to the ones generated by the noisy GA by more than 95%

Real-time emissions from construction equipment compared with model predictions

Este trabalho adotou como aporte teórico, os clássicos de Blumer (1980), Bakhtin (1995), Foucalt (1996) e Galbraith (1986) e os estudos contemporâneos da Comunicação Organizacional pautados no paradigma da produção de sentido (OLIVEIRA; PAULA, 2008) e da gestão sistêmica (YANAZE, 2011) com o objetivo de identificar as relações de poder e luta pelo sentido do projeto social Comunidade Educativa, promovido pela Fundação Bunge na E.E. Henrique Dumont Villares, na capital paulista. Foram realizadas entrevistas qualitativas, em profundidade, com os representantes da Fundação e da Escola e suas narrativas, classificadas em categorias léxico-semânticas de semelhança, complementaridade, diversidade e divergência, sendo posteriormente confrontadas, a fim de identificar o embate dos sentidos. Os resultados apontaram que os interesses particulares pautaram a luta pelo sentido e o poder, que a princípio esteve de posse da Fundação, migrou para o campo dos professores, sendo o significado construído entre estes sujeitos, o hegemônico nesta interação

Real-time emissions from construction equipment compared with model predictions

<div><p>The construction industry is a large source of greenhouse gases and other air pollutants. Measuring and monitoring real-time emissions will provide practitioners with information to assess environmental impacts and improve the sustainability of construction. We employed a portable emission measurement system (PEMS) for real-time measurement of carbon dioxide (CO₂), nitrogen oxides (NO_x), hydrocarbon, and carbon monoxide (CO) emissions from construction equipment to derive emission rates (mass of pollutant emitted per unit time) and emission factors (mass of pollutant emitted per unit volume of fuel consumed) under real-world operating conditions. Measurements were compared with emissions predicted by methodologies used in three models: NONROAD2008, OFFROAD2011, and a modal statistical model. Measured emission rates agreed with model predictions for some pieces of equipment but were up to 100 times lower for others. Much of the difference was driven by lower fuel consumption rates than predicted. Emission factors during idling and hauling were significantly different from each other and from those of other moving activities, such as digging and dumping. It appears that operating conditions introduce considerable variability in emission factors. Results of this research will aid researchers and practitioners in improving current emission estimation techniques, frameworks, and databases.</p><p>Implications: <i>Construction equipment is an important source of air pollutant emissions. There are large uncertainties in estimates of emissions from construction equipment, partly due to the small number of published measurements. The authors have expanded the database by measuring emissions of CO₂, NO_x, hydrocarbons, and CO from construction equipment under actual operating conditions on-site. There were large discrepancies between measured emissions and those predicted by models, including NONROAD and OFFROAD. Emission factors associated with idling and hauling were significantly different from each other and from those of other activities. These results can be used to improve the next generation of emission estimation models.</i></p></div