Using citizen science data to identify the sensitivity of species to human land use

Conservation practitioners must contend with an increasing array of threats that affect biodiversity. Citizen scientists can provide timely and expansive information for addressing these threats across large scales, but their data may contain sampling biases. We used randomization procedures to account for possible sampling biases in opportunistically reported citizen science data to identify species’ sensitivities to human land use. We analyzed 21,044 records of 143 native reptile and amphibian species reported to the Carolina Herp Atlas from North Carolina and South Carolina between 1 January 1990 and 12 July 2014. Sensitive species significantly associated with natural landscapes were 3.4 times more likely to be legally protected or treated as of conservation concern by state resource agencies than less sensitive species significantly associated with human‐dominated landscapes. Many of the species significantly associated with natural landscapes occurred primarily in habitats that had been nearly eradicated or otherwise altered in the Carolinas, including isolated wetlands, longleaf pine savannas, and Appalachian forests. Rare species with few reports were more likely to be associated with natural landscapes and 3.2 times more likely to be legally protected or treated as of conservation concern than species with at least 20 reported occurrences. Our results suggest that opportunistically reported citizen science data can be used to identify sensitive species and that species currently restricted primarily to natural landscapes are likely at greatest risk of decline from future losses of natural habitat. Our approach demonstrates the usefulness of citizen science data in prioritizing conservation and in helping practitioners address species declines and extinctions at large extents.     

Effects of Natura 2000 on nontarget bird and butterfly species based on citizen science data

The European Union's Natura 2000 (N2000) is among the largest international networks of protected areas. One of its aims is to secure the status of a predetermined set of (targeted) bird and butterfly species. However, nontarget species may also benefit from N2000. We evaluated how the terrestrial component of this network affects the abundance of nontargeted, more common bird and butterfly species based on data from long-term volunteer-based monitoring programs in 9602 sites for birds and 2001 sites for butterflies. In almost half of the 155 bird species assessed, and particularly among woodland specialists, abundance increased (slope estimates ranged from 0.101 [SD 0.042] to 3.51 [SD 1.30]) as the proportion of landscape covered by N2000 sites increased. This positive relationship existed for 27 of the 104 butterfly species (estimates ranged from 0.382 [SD 0.163] to 4.28 [SD 0.768]), although most butterflies were generalists. For most species, when land-cover covariates were accounted for these positive relationships were not evident, meaning land cover may be a determinant of positive effects of the N2000 network. The increase in abundance as N2000 coverage increased correlated with the specialization index for birds, but not for butterflies. Although the N2000 network supports high abundance of a large spectrum of species, the low number of specialist butterflies with a positive association with the N2000 network shows the need to improve the habitat quality of N2000 sites that could harbor open-land butterfly specialists. For a better understanding of the processes involved, we advocate for standardized collection of data at N2000 sites.     

Learning and the transformative potential of citizen science

The number of collaborative initiatives between scientists and volunteers (i.e., citizen science) is increasing across many research fields. The promise of societal transformation together with scientific breakthroughs contributes to the current popularity of citizen science (CS) in the policy domain. We examined the transformative capacity of citizen science in particular learning through environmental CS as conservation tool. We reviewed the CS and social‐learning literature and examined 14 conservation projects across Europe that involved collaborative CS. We also developed a template that can be used to explore learning arrangements (i.e., learning events and materials) in CS projects and to explain how the desired outcomes can be achieved through CS learning. We found that recent studies aiming to define CS for analytical purposes often fail to improve the conceptual clarity of CS; CS programs may have transformative potential, especially for the development of individual skills, but such transformation is not necessarily occurring at the organizational and institutional levels; empirical evidence on simple learning outcomes, but the assertion of transformative effects of CS learning is often based on assumptions rather than empirical observation; and it is unanimous that learning in CS is considered important, but in practice it often goes unreported or unevaluated. In conclusion, we point to the need for reliable and transparent measurement of transformative effects for democratization of knowledge production.     

A generalized approach for producing,quantifying, and validating citizen science data from wildlife images

Citizen science has the potential to expand the scope and scale of research in ecology and conservation, but many professional researchers remain skeptical of data produced by nonexperts. We devised an approach for producing accurate, reliable data from untrained, nonexpert volunteers. On the citizen science website www.snapshotserengeti.org , more than 28,000 volunteers classified 1.51 million images taken in a large‐scale camera‐trap survey in Serengeti National Park, Tanzania. Each image was circulated to, on average, 27 volunteers, and their classifications were aggregated using a simple plurality algorithm. We validated the aggregated answers against a data set of 3829 images verified by experts and calculated 3 certainty metrics—level of agreement among classifications (evenness), fraction of classifications supporting the aggregated answer (fraction support), and fraction of classifiers who reported “nothing here” for an image that was ultimately classified as containing an animal (fraction blank)—to measure confidence that an aggregated answer was correct. Overall, aggregated volunteer answers agreed with the expert‐verified data on 98% of images, but accuracy differed by species commonness such that rare species had higher rates of false positives and false negatives. Easily calculated analysis of variance and post‐hoc Tukey tests indicated that the certainty metrics were significant indicators of whether each image was correctly classified or classifiable. Thus, the certainty metrics can be used to identify images for expert review. Bootstrapping analyses further indicated that 90% of images were correctly classified with just 5 volunteers per image. Species classifications based on the plurality vote of multiple citizen scientists can provide a reliable foundation for large‐scale monitoring of African wildlife.     

Conservation mycology in Australia and the potential role of citizen science

Fungi are undoubtedly important for ecosystem functioning; however, they have been omitted or given scant attention in most biodiversity policy documents, management plans, and formal conservation schedules throughout the world. This oversight may be due to a general lack of awareness in the scientific community and compounded by a scarcity of mycology‐associated curricula at the tertiary level and a lack of mycologists in research institutions. Although molecular techniques advance the systematic cataloging of fungi and facilitate insights into fungal communities, the scarcity of professional mycologists in the environmental sciences hampers conservation efforts. Conversely, citizen science initiatives are making significant contributions to the mycology discipline by increasing awareness and extending the scope of fungal surveys. Future research by professional and amateur mycologists into the distribution of fungi and their function in ecosystems will help identify wider and more effective conservation goals.     

Mapping epistemic cultures and learning potential of participants in citizen science projects

The ever‐widening scope and range of global change and interconnected systemic risks arising from people–environment relationships (social‐ecological risks) appears to be increasing concern among, and involvement of, citizens in an increasingly diversified number of citizen science projects responding to these risks. We examined the relationship between epistemic cultures in citizen science projects and learning potential related to matters of concern. We then developed a typology of purposes and a citizen science epistemic‐cultures heuristic and mapped 56 projects in southern Africa using this framework. The purpose typology represents the range of knowledge‐production purposes, ranging from laboratory science to social learning, whereas the epistemic‐cultures typology is a relational representation of scientist and citizen participation and their approach to knowledge production. Results showed an iterative relationship between matters of fact and matters of concern across the projects; the nexus of citizens’ engagement in knowledge‐production activities varied. The knowledge‐production purposes informed and shaped the epistemic cultures of all the sampled citizen science projects, which in turn influenced the potential for learning within each project. Through a historical review of 3 phases in a long‐term river health‐monitoring project, we found that it is possible to evolve the learning curve of citizen science projects. This evolution involved the development of scientific water monitoring tools, the parallel development of pedagogic practices supporting monitoring activities, and situated engagement around matters of concern within social activism leading to learning‐led change. We conclude that such evolutionary processes serve to increase potential for learning and are necessary if citizen science is to contribute to wider restructuring of the epistemic culture of science under conditions of expanding social‐ecological risk.     

Using citizen science butterfly counts to predict species population trends

Citizen scientists are increasingly engaged in gathering biodiversity information, but trade‐offs are often required between public engagement goals and reliable data collection. We compared population estimates for 18 widespread butterfly species derived from the first 4 years (2011–2014) of a short‐duration citizen science project (Big Butterfly Count [BBC]) with those from long‐running, standardized monitoring data collected by experienced observers (U.K. Butterfly Monitoring Scheme [UKBMS]). BBC data are gathered during an annual 3‐week period, whereas UKBMS sampling takes place over 6 months each year. An initial comparison with UKBMS data restricted to the 3‐week BBC period revealed that species population changes were significantly correlated between the 2 sources. The short‐duration sampling season rendered BBC counts susceptible to bias caused by interannual phenological variation in the timing of species’ flight periods. The BBC counts were positively related to butterfly phenology and sampling effort. Annual estimates of species abundance and population trends predicted from models including BBC data and weather covariates as a proxy for phenology correlated significantly with those derived from UKBMS data. Overall, citizen science data obtained using a simple sampling protocol produced comparable estimates of butterfly species abundance to data collected through standardized monitoring methods. Although caution is urged in extrapolating from this U.K. study of a small number of common, conspicuous insects, we found that mass‐participation citizen science can simultaneously contribute to public engagement and biodiversity monitoring. Mass‐participation citizen science is not an adequate replacement for standardized biodiversity monitoring but may extend and complement it (e.g., through sampling different land‐use types), as well as serving to reconnect an increasingly urban human population with nature.     

The potential for citizen science to produce reliable and useful information in ecology

We examined features of citizen science that influence data quality, inferential power, and usefulness in ecology. As background context for our examination, we considered topics such as ecological sampling (probability based, purposive, opportunistic), linkage between sampling technique and statistical inference (design based, model based), and scientific paradigms (confirmatory, exploratory). We distinguished several types of citizen science investigations, from intensive research with rigorous protocols targeting clearly articulated questions to mass-participation internet-based projects with opportunistic data collection lacking sampling design, and examined overarching objectives, design, analysis, volunteer training, and performance. We identified key features that influence data quality: project objectives, design and analysis, and volunteer training and performance. Projects with good designs, trained volunteers, and professional oversight can meet statistical criteria to produce high-quality data with strong inferential power and therefore are well suited for ecological research objectives. Projects with opportunistic data collection, little or no sampling design, and minimal volunteer training are better suited for general objectives related to public education or data exploration because reliable statistical estimation can be difficult or impossible. In some cases, statistically robust analytical methods, external data, or both may increase the inferential power of certain opportunistically collected data. Ecological management, especially by government agencies, frequently requires data suitable for reliable inference. With standardized protocols, state-of-the-art analytical methods, and well-supervised programs, citizen science can make valuable contributions to conservation by increasing the scope of species monitoring efforts. Data quality can be improved by adhering to basic principles of data collection and analysis, designing studies to provide the data quality required, and including suitable statistical expertise, thereby strengthening the science aspect of citizen science and enhancing acceptance by the scientific community and decision makers.     

The role of automated feedback in training and retaining biological recorders for citizen science

The rapid rise of citizen science, with lay people forming often extensive biodiversity sensor networks, is seen as a solution to the mismatch between data demand and supply while simultaneously engaging citizens with environmental topics. However, citizen science recording schemes require careful consideration of how to motivate, train, and retain volunteers. We evaluated a novel computing science framework that allowed for the automated generation of feedback to citizen scientists using natural language generation (NLG) technology. We worked with a photo‐based citizen science program in which users also volunteer species identification aided by an online key. Feedback is provided after photo (and identification) submission and is aimed to improve volunteer species identification skills and to enhance volunteer experience and retention. To assess the utility of NLG feedback, we conducted two experiments with novices to assess short‐term (single session) and longer‐term (5 sessions in 2 months) learning, respectively. Participants identified a specimen in a series of photos. One group received only the correct answer after each identification, and the other group received the correct answer and NLG feedback explaining reasons for misidentification and highlighting key features that facilitate correct identification. We then developed an identification training tool with NLG feedback as part of the citizen science program BeeWatch and analyzed learning by users. Finally, we implemented NLG feedback in the live program and evaluated this by randomly allocating all BeeWatch users to treatment groups that received different types of feedback upon identification submission. After 6 months separate surveys were sent out to assess whether views on the citizen science program and its feedback differed among the groups. Identification accuracy and retention of novices were higher for those who received automated feedback than for those who received only confirmation of the correct identification without explanation. The value of NLG feedback in the live program, captured through questionnaires and evaluation of the online photo‐based training tool, likewise showed that the automated generation of informative feedback fostered learning and volunteer engagement and thus paves the way for productive and long‐lived citizen science projects.     

Evaluating environmental education,citizen science,and stewardship through naturalist programs

Amateur naturalists have played an important role in the study and conservation of nature since the 17th century. Today, naturalist groups make important contributions to bridge the gap between conservation science and practice around the world. We examined data from 2 regional naturalist programs to understand participant motivations, barriers, and perspectives as well as the actions they take to advance science, stewardship, and community engagement. These programs provide certification‐based natural history and conservation science training for adults that is followed by volunteer service in citizen science, education, and stewardship. Studies in California and Virginia include quantitative and qualitative evaluation data collected through pre‐ and postcourse surveys, interviews, and long‐term tracking of volunteer hours. Motivations of participants focused on learning about the local environment and plants and animals, connecting with nature, becoming certified, and spending time with people who have similar interests. Over half the participants surveyed were over 50 years old, two‐thirds were women, and a majority reported household incomes of over $50,000 (60% in California, 85% in Virginia), and <20% of those surveyed in both states described themselves as nonwhite. Thus, these programs need to improve participation by a wider spectrum of the public. We interviewed younger and underrepresented adults to examine barriers to participation in citizen science. The primary barrier was lack of time due to the need to work and focus on career advancement. Survey data revealed that participants’ ecological knowledge, scientific skills, and belief in their ability to address environmental issues increased after training. Documented conservation actions taken by the participants include invasive plant management, habitat restoration, and cleanups of natural areas and streams. Long‐term data from Virginia on volunteer hours dedicated to environmental citizen science show an increase from 14% in 2007 to 32% in 2014. In general, participants in the naturalist programs we examined increased their content knowledge about ecosystems, had greater confidence in conserving them, and continued to engage as citizen scientists after completing the program.     

Emerging contaminant control: From science to action

Since the concept of emerging contaminants (ECs) was first proposed in 2001, the global scientific research of ECs has developed rapidly. In the past decades, great progress has been achieved in the scientific research of ECs in China, including the establishment of EC analysis method system, the evaluation of the pollution status, pollution characteristics and environmental risk of ECs in typical regions of China, and establishment of EC control technology system. Continuous progress in scientific research of ECs promoted China’s action on EC control. It is planned that the environmental risk of ECs will be generally controlled by 2035 in China. Priority ECs should be screened for environmental management. Although great efforts have been made, the EC control in China still faces tremendous challenges. It is necessary to bridge the gap between scientific research and decision-making management. Based on the science and technology study, various measures such as engineering, policy management and public participation should be combinedly adopted for EC control.     

Drivers of support for citizen science across state wildlife management agencies in the United States

Citizen science (CS) is gaining recognition as a valuable approach to meet data needs for environmental projects while fostering collaboration between scientists and members of the public. Despite increasing implementation of CS by natural resource entities, organizations’ motivations for engaging in CS remain poorly understood. We examined the utility of the theory of planned behavior (TPB) and social exchange theory (SET) in identifying factors influencing support of CS by scientific organizations. To test predictions of the TPB and SET theories, we surveyed (quantitative, web based) state wildlife agency staff in the United States on their perceptions of organizational engagement in CS. We divided questions that measured TPB items into individual and organizational components to address the influence of personal- and organization-level decision-making on staff perceptions and attitudes. We used structural equation modeling to identify key constructs that influence staff support of CS in state wildlife agencies. The survey yielded 627 responses across 44 states. Both TPB and SET constructs accurately predicted staff support of CS; however, measures from SET (e.g., public engagement benefits and costs of CS to scientific credibility) were most influential (i.e., TPB constructs had less impact). Our findings indicate that organizational support for CS is primarily influenced by assessment of trade-offs among perceived costs and benefits. Indicators of support for CS were further elucidated by including measures from the TPB model. Based on our results, we suggest that natural resource entities give careful consideration to CS project design, develop thorough communication and data management plans, and practice iterative evaluation of CS project productivity.     

A framework for evaluating and designing citizen science programs for natural resources monitoring

We present a framework of resource characteristics critical to the design and assessment of citizen science programs that monitor natural resources. To develop the framework we reviewed 52 citizen science programs that monitored a wide range of resources and provided insights into what resource characteristics are most conducive to developing citizen science programs and how resource characteristics may constrain the use or growth of these programs. We focused on 4 types of resource characteristics: biophysical and geographical, management and monitoring, public awareness and knowledge, and social and cultural characteristics. We applied the framework to 2 programs, the Tucson (U.S.A.) Bird Count and the Maui (U.S.A.) Great Whale Count. We found that resource characteristics such as accessibility, diverse institutional involvement in resource management, and social or cultural importance of the resource affected program endurance and success. However, the relative influence of each characteristic was in turn affected by goals of the citizen science programs. Although the goals of public engagement and education sometimes complimented the goal of collecting reliable data, in many cases trade‐offs must be made between these 2 goals. Program goals and priorities ultimately dictate the design of citizen science programs, but for a program to endure and successfully meet its goals, program managers must consider the diverse ways that the nature of the resource being monitored influences public participation in monitoring.     

Studying citizen science through adaptive management and learning feedbacks as mechanisms for improving conservation

Citizen science has generated a growing interest among scientists and community groups, and citizen science programs have been created specifically for conservation. We examined collaborative science, a highly interactive form of citizen science, which we developed within a theoretically informed framework. In this essay, we focused on 2 aspects of our framework: social learning and adaptive management. Social learning, in contrast to individual‐based learning, stresses collaborative and generative insight making and is well‐suited for adaptive management. Adaptive‐management integrates feedback loops that are informed by what is learned and is guided by iterative decision making. Participants engaged in citizen science are able to add to what they are learning through primary data collection, which can result in the real‐time information that is often necessary for conservation. Our work is particularly timely because research publications consistently report a lack of established frameworks and evaluation plans to address the extent of conservation outcomes in citizen science. To illustrate how our framework supports conservation through citizen science, we examined how 2 programs enacted our collaborative science framework. Further, we inspected preliminary conservation outcomes of our case‐study programs. These programs, despite their recent implementation, are demonstrating promise with regard to positive conservation outcomes. To date, they are independently earning funds to support research, earning buy‐in from local partners to engage in experimentation, and, in the absence of leading scientists, are collecting data to test ideas. We argue that this success is due to citizen scientists being organized around local issues and engaging in iterative, collaborative, and adaptive learning.     

Place‐based and data‐rich citizen science as a precursor for conservation action

Environmental education strategies have customarily placed substantial focus on enhancing ecological knowledge and literacy with the hope that, upon discovering relevant facts and concepts, participants will be better equipped to process and dissect environmental issues and, therefore, make more informed decisions. The assumption is that informed citizens will become active citizens––enthusiastically lobbying for, and participating in, conservation‐oriented action. We surveyed and interviewed and used performance data from 432 participants in the Coastal Observation and Seabird Survey Team (COASST), a scientifically rigorous citizen science program, to explore measurable change in and links between understanding and action. We found that participation in rigorous citizen science was associated with significant increases in participant knowledge and skills; a greater connection to place and, secondarily, to community; and an increasing awareness of the relative impact of anthropogenic activities on local ecosystems specifically through increasing scientific understanding of the ecosystem and factors affecting it. Our results suggest that a place‐based, data‐rich experience linked explicitly to local, regional, and global issues can lead to measurable change in individual and collective action, expressed in our case study principally through participation in citizen science and community action and communication of program results to personal acquaintances and elected officials. We propose the following tenets of conservation literacy based on emergent themes and the connections between them explicit in our data: place‐based learning creates personal meaning making; individual experience nested within collective (i.e., program‐wide) experience facilitates an understanding of the ecosystem process and function at local and regional scales; and science‐based meaning making creates informed concern (i.e., the ability to discern both natural and anthropogenic forcing), which allows individuals to develop a personalized prioritization schema and engage in conservation action.     

How to address data privacy concerns when using social media data in conservation science

Social media data are being increasingly used in conservation science to study human–nature interactions. User-generated content, such as images, video, text, and audio, and the associated metadata can be used to assess such interactions. A number of social media platforms provide free access to user-generated social media content. However, similar to any research involving people, scientific investigations based on social media data require compliance with highest standards of data privacy and data protection, even when data are publicly available. Should social media data be misused, the risks to individual users' privacy and well-being can be substantial. We investigated the legal basis for using social media data while ensuring data subjects’ rights through a case study based on the European Union's General Data Protection Regulation. The risks associated with using social media data in research include accidental and purposeful misidentification that has the potential to cause psychological or physical harm to an identified person. To collect, store, protect, share, and manage social media data in a way that prevents potential risks to users involved, one should minimize data, anonymize data, and follow strict data management procedure. Risk-based approaches, such as a data privacy impact assessment, can be used to identify and minimize privacy risks to social media users, to demonstrate accountability and to comply with data protection legislation. We recommend that conservation scientists carefully consider our recommendations in devising their research objectives so as to facilitate responsible use of social media data in conservation science research, for example, in conservation culturomics and investigations of illegal wildlife trade online.     

Citizen science networks in natural history and the collective validation of biodiversity data

Biodiversity data are in increasing demand to inform policy and management. A substantial portion of these data is generated in citizen science networks. To ensure the quality of biodiversity data, standards and criteria for validation have been put in place. We used interviews and document analysis from the United Kingdom and The Netherlands to examine how data validation serves as a point of connection between the diverse people and practices in natural history citizen science networks. We found that rather than a unidirectional imposition of standards, validation was performed collectively. Specifically, it was enacted in ongoing circulations of biodiversity records between recorders and validators as they jointly negotiated the biodiversity that was observed and the validity of the records. These collective validation practices contributed to the citizen science character or natural history networks and tied these networks together. However, when biodiversity records were included in biodiversity‐information initiatives on different policy levels and scales, the circulation of records diminished. These initiatives took on a more extractive mode of data use. Validation ceased to be collective with important consequences for the natural history networks involved and citizen science more generally.     

Soil quality: a review of the science and experiences in the USA

An increasing human population is placing greater demand on soil resources, and as a result degradation is taking place in many regions of the world. This is critical because soils perform a number of essential processes including supporting food and fiber production, influencing air quality through interaction with the atmosphere, and serving as a medium for storage and purification of water. The soil quality concept was introduced to complement soil science research by making our understanding of soils more complete and helping guide the use and allocation of labor, energy, fiscal, and other inputs as agriculture intensifies and expands to meet increasing world demands. Soil quality thus provides a unifying concept for educating professionals, producers, and the public about the important processes that soils perform. It also provides an assessment tool for evaluating current management practices and comparing alternative management practices. Soil attributes comprising a minimum data set have been identified, and both laboratory and field methods have been developed for measuring them. A soil quality index is being developed to normalize measured soil quality indicator data and generate a numeric value that can be used to compare various management practices or to assess management-induced changes over time. Using previously published data, we evaluated the soil quality index as a tool to assess a wide range of management practices in the Northern Great Plains. The index ranked the treatments: grazed fertilized tame pasture > moderately grazed > ungrazed > heavily grazed > annual cropping with no-tillage > conventionally tilled crop-fallow which agrees with the way they were subjectively ranked in the publications. The soil quality index shows potential for use as a management assessment tool.     

Three-way compositional analysis of water quality monitoring data

Water quality monitoring data typically consist of \(J\) parameters and constituents measured at \(I\) number of static locations at \(K\) sets of seasonal occurrences. The resulting \(I \times J \times K\) three-way array can be difficult to interpret. Additionally, the constituent portion of the dataset (e.g., major ion and trace element concentration, pH, etc.) is compositional in that it sums to a constant (e.g., 1 kg/L) and is mathematically confined to the simplex, the sample space for compositional data. Here we apply a Tucker3 model on centered log-ratio data to find low dimensional representation of latent variables as a means to simplify data processing and interpretation of three years of seasonal compositional groundwater chemistry data for 14 wells at a study site in Wyoming, USA. The study site has been amended with treated coalbed methane produced water, using a subsurface drip irrigation system, to allow for irrigation of forage crops. Results from three-way compositional data analysis indicate that primary controls on water quality at the study site include: solutes concentration by evapotranspiration, cation exchange, and dissolution of native salts. These findings agree well with results from more detailed investigations of the site. In addition, the model identified Ba uptake during gypsum precipitation in some portions of the site during the final 6–9 months of investigation, a process for which the timing and extent had not previously been identified. These results suggest that multi-way compositional analyses hold promise as a means to more easily interpret water quality monitoring data.     

A building-based data capture and data mining technique for air quality assessment

Recently, a building-based air quality model system which can predict air quality in front of individual buildings along both sides of a road has been developed. Using the Macau Peninsula as a case study, this paper shows the advantages of building-based model system in data capture and data mining. Compared with the traditional grid-based model systems with input/output spatial resolutions of 1–2 km, the building-based approach can extract the street configuration and traffic data building by building and therefore, can capture the complex spatial variation of traffic emission, urban geometry, and air pollution. The non-homogeneous distribution of air pollution in the Macau Peninsula was modeled in a high-spatial resolution of 319 receptors·km^-2. The spatial relationship among air quality, traffic flow, and urban geometry in the historic urban area is investigated. The study shows that the building-based approach may open an innovative methodology in data mining of urban spatial data for environmental assessment. The results are particularly useful to urban planners when they need to consider the influences of urban form on street environment.