Urban air pollution is well-known to cause negative health impacts. Therefore, the monitoring of pollutant concentration levels plays a key role in understanding air quality and its effects on the subjective well-being (SWB) of citizens (Laffan 2018). SWB reflects the philosophical notion of a good life, as a proxy for assessing life satisfaction, momentary experiences, and stress. Kim-Prieto et al. (2005) also took into account contemporary health hazards, among which air pollution is a key factor (Ferreira et al., 2013, Signoretta et al., 2019). To promote public well-being while protecting the environment, the UN has targeted 17 Sustainable Development Goals (SDGs) and their indicators (Koch and Krellenberg 2018) to track the overall progress towards 2030. SDGs have become fundamental strategies to guide the world’s social and economic transformation (Shi et al., 2019), putting emphasis on respecting natural resources and the needs of future generations. Of the SDGs, the 11th SDG is targeted at reducing the adverse per capita environmental impact of cities, including paying special attention to air quality; additionally, SDG target 3.9 aims to reduce the number of illnesses, among others, from air pollution (UN, 2015). The development of smart and sustainable cities can only be accomplished through inclusive growth, using smart people, technologies, and policies (Yigitcanlar et al., 2019). From the perspective of Smart People (Giffinger et al., 2007)—those who use smart devices to make their everyday living easier and health safer—we found it necessary to develop GeoWeb solutions to measure the adverse impact of cities on their inhabitants. Ensuring measurement credibility becomes a key scientific challenge in this context. To this end, we carried out research on the example of air pollution health symptoms, an emerging trend particularly related to odour (Arias et al., 2018) and green pollutant (Bastl et al., 2015) crowdsensing (Dutta et al., 2017, Feng et al., 2018). As crowdsensing (or, more generally, crowdsourcing) methods for health symptom mapping are subject to data bias (Zupančič and Žalik 2019), we developed and tested the quality assurance mechanism (QAm) framework (section 2.2), which can be transferred to similar health symptom-based studies. From a practical point of view, we use a case study crowdsourcing data set to track the progress on SDG 11 with the use of tier 3 SDG 11.6 indicators (Koch and Krellenberg 2018). The tier structure of the SDG indicator system defines tier 3 as a group of indicator candidates for which no agreed measurement methodology is available; we use this tier to propose a measure of citizen SWB with respect to adverse per capita air pollution impact.
Sparse or irregular monitoring station networks as well as limited access to the reference air pollution data underlies the need for CS activities in the field of air pollution monitoring. Personalized information on exposure to air pollutants, monitoring during acute events or at specific locations, partnerships with local governments, and educational and community-driven purposes are the key benefits of bottom-up environmental monitoring. CS enables the collection of data on much larger spatial and temporal scales and at much finer resolution than would otherwise be possible. The issue of urban air pollution crowdsourcing motivated the implementation of several citizen science (CS) programs, such as those led by Mapping for Change, a community interest company in London (e.g., Pepys Air Quality Project, Science in the City Project, Love Lambeth Air; mappingforchange.org.uk/projects/), as well other international-scale activities such as those found at claircity.eu and citi-sense.eu (Castell et al., 2015), whenever hosted on GeoWeb (Komarkova et al., 2007, Haklay 2013, Jankowski et al., 2019). In this approach, citizens are required to act as sensors (Goodchild 2007), as “traditional data sources are not sufficient for measuring the SDGs” (Fritz et al., 2019). The SDG tier 3 indicator system is expected to use known quality crowdsourced data to track the progress of sustainable development. In this study, we specifically focus on urban air pollution and its health-related symptoms.
By default, urban air pollution is determined using a national, state, or local monitoring network of digital sensors measuring particulate matter (PM) 2.5 μm and 10 μm, NO2, and SO2 concentration levels. The link between air pollution and human health hazards has been proven both in the field of environmental science (Brągoszewska and Biedroń 2018, Weryszko-Chmielewska et al., 2018, Hano et al., 2019), as well as by medical research (Grzybowski and Mimier 2019, Warburton et al., 2019). However, anthropogenic-sourced PMs are not the sole factors of air pollutants (discussed in detail in Section 1.2). The first attempts to crowdsource urban air pollution data, understood as the compound effect of anthropogenic- and biophysical-sourced PM, were undertaken in the CS HackAir project (https://www.hackair.eu), as well the PollenApp (Bastl et al., 2015). Pan-European CS projects, such as D-NOSES (Distributed Network for Odour Sensing Empowerment and Sustainability; Arias et al., 2018), have shown that particular aspects of urban air pollution can be measured through the sense of smell of trained citizen scientists. In general, the use of a “sense of smell” is not a new approach in air pollution research. Its beginnings dates back to 1794, when Jean-Noël Hallé (the Chair of Public Hygiene in Paris) developed ‘smell walking and mapping’ as a methodology for identifying environmental hazards in industrializing cities (Kitson et al., 2019). Today, poor human olfaction is a 19th-century myth, derived from neuroanatomist Paul Broca’s hypothesis that the evolution of human free will requires a reduction in the proportional size of the brain’s olfactory bulb. In fact, such a reduction in size has been shown to not be accompanied by a reduction in the number of neurons it contains. Thus, humans have excellent olfactory ability (McGann 2017). This ability provides a scientific basis for research in the field of mammalian space sensors (Nummela et al., 2013, Meister 2015), multisensory landscape perception (smell-scape; Porteous 1985, Gorman 2017, Xiao et al., 2018), and odour crowd-sensing (Arias et al., 2018).
Contemporary scientific odour pollution mapping (Eltarkawe and Miller 2018) is carried out by the use of geo-survey (Engel et al., 2018) or a field olfactometer, a calibrated measurement tool (Walgraeve et al., 2015, Motalebi Damuchali and Guo, 2019), which dilutes air samples such that the human sense of smell can sense the odour intensity at a minimum threshold of concentration. Olfactometer calibration aims to reduce the subjective nature of odour sensing. Preliminary results of field olfactometer mapping have been reported by Walgraeve et al., (2015), Badach et al., (2018), and Kitson et al., (2019); however, this measurement tool has not yet been recognized as citizen science equipment. Moreover, citizen science has been recognized as a separate method of odour measurement (Bax et al., 2020).
Despite the subjective measurement nature of the human sense of smell, human-sensed CS (sCS) meets the high-quality method expectations (D-NOSES meets VDI (germ. Verein Deutscher Ingenieure, eng. The Association of German Engineers) 3940 standard). Human-sensed measurements of ambient air pollution, as well as the symptoms it causes, provide a promising source of spatial information. However, the unstructured nature of crowdsourced data (Capineri et al., 2016, Kamp et al., 2016, Moreri et al., 2018) requires data quality assurance (QA) protocols (Wiggins et al., 2011, Kosmala et al., 2016), as well as trust and reputation modelling (TRM; Bishr and Mantelas 2008) procedure development.
To the best of our knowledge, using QA for the purpose of air pollution symptom mapping (APSM) has not yet been investigated. Our goal was to address the challenge of crowdsourced APSM framework design with the use of a GeoWeb platform (Section 2.3) and to solve the data bias problem by using a quality assurance logic-rules-based system (QAs; Section 2.2). By assessing the rejection rate of reports affected by data bias, we provide evidence of reliable air pollution monitoring expressed as the severity of human health symptoms caused by combined factors of anthropogenic and biophysical ambient air pollutants (Grewling et al., 2019). Extending the paradigm of air pollution referenced by the WHO in terms of six main air pollutant (Sheng and Tang 2016) concentrations levels and their public health impacts (WHO 2005) to air pollution symptoms (APSs), we indicate new possibilities for citizen-driven research and social inclusion in environmental and health-related issues, as experienced by sustainable cities. We also contribute to the development of an sCS data quality methodology. Furthermore, the quality of the spatial data determines its usability in the field of SDG indicators (OWA 2015, Hecker et al., 2018).
1.1. Sustainable Development Goal Partnership on Urban Air Pollution
Sustainability is an interactive process, maintaining a dynamic balance among six dimensions—land, natural environment, institutions, technology, economics, and humans—where change within one dimension has an impact on the others (Dockry et al., 2016). These changes and impacts may occur globally, which is why GIScience (Goodchild, 2009) plays a leading role in achieving the global SDGs.
The general idea for the SDGs has been expressed, by Brundtland et al. (1987), as the need to “meet the necessities of the present generation without harming the future generation's capacity”. We consider health symptoms caused by air pollution as one of the indicators of the current ecological footprint of humanity on the environment. In 2000, Brundtland’s idea was formulated as the Millennium Development Goals by the UN (Sachs, 2012). Over the next 15 years, the idea was emphasized as the interconnected environmental, health, social, and economic aspects of development (Schleicher et al., 2018) in SDG 2030. Out of the 17 SDGs, the 11th SDG refers to sustainable cities and communities and the third SDG refers to public health. In both cases, poor air quality caused by ambient air pollutants (in particular, referring to SDG target 11.6) is a key issue, which provided the motivation for this research. The Organization for Economic Cooperation and Development (OECD) predicts that, by 2050, air pollution will be the main reason for human mortality (Marchal et al., 2012). Therefore, we focused on urban air pollution as a case study with the starting point of health symptoms caused by human exposure to air pollutants. This concept highlights the relationship between urban habitats and SWB of citizens, as well as the interdependence of the particular SDGs. The issue of air pollution requires spatial information provided thoroughly by a modern spatially variable society (Enemark and Rajabifard, 2011, Ionita et al., 2015). This underlines the need to implement a local partnership between air pollution monitoring agencies, researchers, and the local community, who all breathe the same air. Therefore, high quality geospatial data, in terms of air pollution, is expected to be used for monitoring global progress towards achieving the SDGs.
The need for open geospatial technologies for measuring SDG 11.6 has been discussed by Choi et al. (2016), where special attention was paid to sCS as a public–academic partnership, where citizens collect data, which is then used by research institutions and themselves. By engaging in APSM, the project members, as citizen scientists (Bonney et al., 2016), facilitate the implementation of the SDGs to become an integral part of social innovation.
1.2. Extending the Paradigm of Urban Air Pollution
In the field of environmental research air quality, information on the quality (i.e., clean or polluted) of air is reported as an air quality index (AQI; Liu et al., 2019). AQI tracks six major air pollutants, inhalable particles (PM10), fine particulate matter (PM2.5), ozone (O3), sulfur dioxides (SO2), nitrogen dioxides (NO2), and carbon monoxide (CO; Sheng and Tang, 2016). The spectrum of pollutant sources includes those related to the development of human civilization (anthropogenic pollutants; Grewling et al., 2019), as well those from natural sources, which questions the belief that everything that is natural is healthy (Liang, 2013). Ambient air pollution concentrations above the approved limits (Kelly and Fussell, 2015, Zwozdziak et al., 2016) can cause certain health symptoms. Conversely, health symptoms can reflect air pollution. However, health symptoms resulting from inhalation of polluted air are also stimulated by natural-sourced biophysical PM such as pollen, mold spores (Bastl et al., 2017, Grewling et al., 2019), and volcanic emissions (Joseph et al., 2019), causing human health problems such as respiratory allergies including allergic asthma, which is regarded as an important disease (Baldacci et al., 2015, Di Menno di Bucchianico et al., 2019, Grewling et al., 2019). In terms of air quality, aerobiologists focus on plant species whose pollen is most harmful for pollen allergy sufferers (e.g., birch, alder, mugwort, grass, and so on) and emphasize that their co-occurrence with PMs is affected by other factors, such as increasing urban air temperature (Grewling et al., 2019).
Bastl et al. (2015, 2017) described pollen as one of the “green pollutants”, which are significant components of the atmosphere and are relevant to air quality information for pollen allergy sufferers. This distinction is important for the comprehensive understanding of APS. Air pollution is specified as the concentration of pollutants measured in physical values (e.g., micrograms per cubic meter), whereas air quality refers to AQI, as well as to classifications, opinions, and feelings, including the experiences of citizens in terms of air - and air quality-related SWB (Laffan, 2018, Signoretta et al., 2019). This broad understanding of air quality is accepted in ecosystem services science, where poor air quality is referred to as an ecosystem disservice (Escobedo et al., 2011, Sacchi et al., 2017). This concept extends our understanding of air pollution from pollutant concentration levels to personal health symptoms caused by pollutant inhalation. The quantity and severity of symptoms can explain the air quality; however, consensus about the terminology involving urban air quality has not yet been reached and researchers typically distinguish air pollution through pollen exposure (McInnes et al., 2017). There is no symptom classification for air quality yet. Regardless, both factors shape air quality. Future research is required to understand and quantify the interaction of co-exposure to both types of air pollutants and its impact on the severity of human health symptoms (Robichaud and Comtois, 2019).
Symptom mapping is a prerequisite for the spatial explanation of both dependencies. First attempts of citizen symptom mapping related to green pollutants have been undertaken by Bastl et al. (2017) and Werchan et al. (2017). Their research proved that citizen symptom load can be mapped efficiently using crowdsourced data; however, the sources of the symptoms cannot be clearly determined. The symptom load index is not directly correlated with annual pollen loads and has a strong correlation to allergen content (Bastl et al., 2017), with an (often daily) linear correlation (Bastl et al., 2017, Bédard et al., 2020). Finding that relationship is beyond the scope of this paper; however, crowdsourced symptom data have shown potential as an indicator of the effects of urban air pollution on citizen well-being. This raises the possibility for new tier 3 SDG indicator, as monitored following a standardized CS method. The unstructured nature of crowdsourced data requires rigorous QA mechanisms. In this study, our aim is to identify QA system for APSM and provide a GeoWeb framework to stream high-quality data in order to facilitate a tier 3 SDG indicator system. So far, this data stream does not exist. By sharing trusted and open data on air pollution symptoms, our findings can be used for aerobiological and health risk forecasting research.
1.3. Contribution of Citizen Science to Improvements in Air Pollution Mapping
According to Haklay (2013), geographical citizen science overlaps VGI, especially in the geographical context of citizen-driven research. GeoWeb plays an essential role in this field. However, it is crucial that CS and VGI should not be seen as equal, as the main purpose of VGI is to produce geographical information, whilst citizen science aims to produce new scientific knowledge (Connors et al., 2012, Eitzel et al., 2017). Citizens engaged in scientific research projects become citizen scientists (Silvertown, 2009) who, depending upon their personal interests, motivation, education level, and experience in previous projects, engage with different levels of participation and expect to see the results of their research contribution. They contribute in the project by collecting and analysing data, but may also be involved in defining research questions or even interpreting results (Dickinson et al., 2010, Haklay, 2013, Kar et al., 2016). Considering the scope of citizen participation, Haklay (2013) has defined four levels of CS: crowdsourcing (first level), distributed intelligence (second level), participatory science (third level), and extreme citizen science (fourth level). Citizen involvement in environmental projects on air pollution is usually based on collecting and analysing sensor data in the form of online maps. In this way, knowledge is produced. The fundamental questions about the harmful health effects of air pollutant have been asked, so these activities are typified as CS level 1 and CS level 2. Of course, higher levels (depending on the engagement of members) are not excluded. In the case of odour crowdsourcing, which requires training as well as expecting measurement insights back from members, a collection method can be devised (i.e., level 3). Reviewing the most relevant air pollution citizen science activities (Table 1), the typology of participation engagement can be assigned to be basic on the project description; however, this does not limit the engaged members to achieve the next levels through the re-use of data, scientific collaboration, and report publishing. Our study was based on the first level of CS, where citizens are engaged in the process of crowdsourcing APS data to monitor progress toward the achievement of SDGs 11.6.3, producing a new scientific knowledge of APSM together with researchers. CS provides a solution to research problems while also educating citizens (Bonney et al., 2009). Before starting to collect data in this study, citizens were educated about the research problem and project aims and were trained how to use the associated tools properly. By attending workshops, the citizens gained knowledge and new skills, and followed the progress of the project in real-time. By sharing their conclusions and opinions during the social campaign, they had a direct impact on the optimization of methods used.
So far, smartphones have not been considered appropriate equipment for measuring urban air pollution. This is due to the fact that the built-in sensors of smartphones, by default, do not allow users to measure air pollutant concentrations. Therefore, bottom-up activities considering air pollution have usually relied on external, low-cost sensors (initially only capable of PM measurement, these sensors can now also sense all major pollutants, including volatile organic compounds). In an attempt to involve smartphones users into air pollution monitoring, efforts have been made to determine the PM concentration with the use of a mobile app which takes images of clear blue skies (AirTick project), with an average of day time PM1 concentration level up to 87% (Zhu et al., 2018). Other approaches have used spectropolarimeters as add-ons, such as within the iSpex project (Snik et al., 2014), to measure PM concentration level. The idea of using a smartphone camera to measure air pollution has been adopted by the HackAir project (Kosmidis et al., 2018). Furthermore, the most recent smartphone cameras and flash function-based development of a fine dust measurement system called FeinPhone (Budde et al., 2019) suggests that low-cost PM sensors may become default equipment in next-generation smartphones. Low-cost and relatively good result correlations with reference air pollution stations (Karagulian et al., 2019) allows users to set up citizen science initiatives and involve local communities into global problem solving. The most relevant of these projects are listed in Table 1, which is an extension of the review carried out by Moumtzidou et al. (2016). The relatively simple design of citizen science sensors makes them suitable for do-it-yourself (DiY) workshops. Creating local workshop groups, usually co-ordinated by a local Media Lab, allows the establishment of communities which are emotionally involved in self-created monitoring networks, which becomes the basic mechanism motivating the continuation of the local monitoring project. Furthermore, the growing awareness of air pollution hazards has led to the development of personal sampler devices (e.g., PlumeLab) designed to be mobile and facilitating real-time monitoring of exposure to air pollution; such new smart devices could be used effectively in citizen science activities. Coupled with the application to health symptom recording, they could progress our understanding of air pollutants, their co-existence, and their relationships with human health (Bédard et al., 2020). Citizen measurements were formerly conducted in a stationary manner through the use of passive diffusion tubes (Palmes et al., 1976) or wipes for pollution measurement; at present, such measurements can successfully be carried out in a mobile way through the use of smart sensors. Loreto et al. (2017) emphasized that modern participatory sensing, which is one of three sub-categories of citizen cyberscience (Grey, 2009), has witnessed significant progress related to the fast development and social networking tools of ICT (Information and Communication Technologies), which “allow effective data and opinion collection and real-time information sharing processes”. In that context, Guo et al. (2015) and Capponi et al. (2019) introduced mobile crowdsensing (MCS), which focuses on sensing and collecting data with mobile devices and aggregating data in the cloud. However, there are pollutants which are still exclusive for IoT 'sensor dust'. A great challenge of contemporary CS measurement is odour sensing, which affects both indoor as well as outdoor air quality. Human-sensed air pollution monitoring seems to be an emerging trend.
Table 1 A review of relevant citizen science initiatives for air pollution monitoring. Explanation: GeoWeb: M – mobile app, W – web app, e – educational resources; Sensors: D – digital toolkit of low-cost sensors, DiY – do-it-yourself sensors, Md – measured by mobile devices (surveys, video, image, voice), Hs – human senses; Feedback to Citizen Scientists: Rt – real-time mapping, Pc – personalized communication, Ms – map screening, Ru – re-use of data; No info – information is not available. Sources: Air Quality Egg (airqualityegg.wickeddevice.com/); PlumeLab personal sampler (plumelabs.com); Smart Citizen Kit (smartcitizen.me); Aircitizen (aircitizen.org); AirCasting (habitatmap.org); Sensebox (sensebox.de); CitiSense (co.citi-sense.eu); CaptorAir (www.captor-project.eu).
Project name
|
Aim of the project
|
Pollutants
|
GeoWeb
|
Sensors
|
Feedback to the citizens
|
Group of projects:
Air Quality Egg; Smart Citizen Kit; AirCitizen; Plumelabs; AirCasting; SenseBox
|
The education and inclusion of local communities in air pollution monitoring with the use of low-cost sensors and open-source WebGIS
|
PM2.5; PM10
SO2; NO2; CO; O3;
VOC
|
W, e
|
D
|
Rt
|
CitiSense (CityAir App)
|
To build European network of low-cost DiY air pollution sensors
|
PM2.5; PM10
SO2; NO2; NO; O3
|
W, M, e
|
DiY; Hs
(AQ perception)
|
Rt
|
Luftdaten;
AirsensEur (Gerboles et al., 2015)
|
PM2.5; PM10
SO2; NO2; NO; O3
|
W, e
|
DiY
|
Rt
|
D-Noses (Arias et al., 2018)
|
To create a community map of odour and provide a bottom-up approach to tackling odour pollution issues.
|
Outdoor odour
|
W, M, e
|
Hs (trained volunteers)
|
Ms
|
IAQ self reporting (Similä et al., 2019)
|
Collect long-term perceived indoor air quality data and symptoms to monitor school air quality.
|
Indoor odour
|
M
|
Hs
|
Pc
users push notifications
|
Innovation Program for Environmental Monitoring
(IPEM; Wesseling et al., 2019)
|
To build a crowdsourced system that provides citizens with detailed environmental data and enriches the Dutch environmental monitoring network.
|
PM2.5; PM10
SO2; NO2
|
W
|
Dt
|
Ms
|
CaptorAir
|
Three-year project aimed at monitoring ozone pollution in Spain, Italy, and Austria with the use of low-cost sensors
|
O3
|
W
|
Dt
|
Ms
|
In this research, we specify the “citizens as sensors” and participatory sensing concepts, where the senses, subjective impressions, and perception of humans are the only sensors used in the project; therefore, we propose this as human-sensed CS. Moreover, by developing a QA mechanism for sCS, this study contributes to bottom-up air pollution monitoring and open data credibility.
Personal symptom observations are an everyday practice of pollen allergy sufferers. They have access to smartphone applications such as Patient’s Hayfever Diary (PHD; available as “Pollen App”; Bastl et al., 2015), MASK (Mobile Airways Sentinel Network) Allergy Diary (Bousquet et al., 2017), or other digital allergy diaries (Voorend-van Bergen et al., 2014, Bastl et al., 2017) which help to monitor, analyse, and understand personal health symptoms. In this study, we wanted to involve the personal symptoms observations of citizens. The bottom-up approach is consistent with the CS definition: co-operation of citizens (non-experts) and scientists (professionals) for the solution of research problems in a specific area of science (Kar et al., 2016).
1.4. Importance of Data Quality in Crowdsourced Air Pollution
Data quality issues include errors and biases. Factors affecting the data collected through citizen perceptions result in data biases. Citizen Science requires the collaborative contributions of multiple contributors (Haklay et al., 2010), but the assumption of multiple contributors is insufficient to provide high-quality data. Therefore, data quality protocols are an essential part of crowdsourcing-driven research. Although participatory research faces methodological challenges such as biases in data collection (Nimbalkar and Tripathi, 2016, English et al., 2018), CS has been proven to be a source of trusted geospatial data (Sheppard and Terveen, 2011, Lin et al., 2015, Kosmala et al., 2016, Fritz et al., 2017, Parrish et al., 2018), including for health risk mapping (Maantay, 2007, Keddem et al., 2015, Palmer et al., 2017) and risks caused by poor air quality (Bastl et al., 2017, Penza et al., 2017, Khasha et al., 2018, Kankanamge et al., 2019). The data quality determines its usefulness (Choi et al., 2016, Chmielewski et al., 2018). Thus, the unstructured nature of crowdsourced data requires rigorous data QA protocols (Flanagin and Metzger, 2008, Antoniou and Skopeliti, 2015, Foody et al., 2018, Moreri et al., 2018, Wu et al., 2018).
In CS air quality projects, data quality assurance methods have been developed for combining low-cost personal digital or mobile sensor data with data from the official air pollution monitoring stations or local-scale air pollution models (Van den Bossche et al., 2015, Miskell et al., 2017, Schneider et al., 2017), or even with data mined from social media posts (Jiang et al., 2015, Sun et al., 2017, Zheng et al., 2018). To optimize the data quality of digital sensors, pre- and post-sampling calibration adjustments are typically applied, such as temperature corrections and filter equilibrations (Gillooly et al., 2019). Castell et al. (2017) and Spinelle et al. (2017) emphasized that the field calibration of the low-cost devices remains a challenge.
Sensor measurements are still valuable, despite their limited precision and accuracy. Low-cost sensors should be only considered good enough for the intended objective (Williams et al., 2014) and, as part of the trust and reputation modelling (TRM) procedure, should include metadata for characterizing the exact qualities of the recorded data (Clements et al., 2017). The D-NOSES project (Arias et al., 2018) proved that the sense of smell of individuals can be calibrated through training on odour pollution and workshops exploring odour perception in the D-NOSE method.
The starting point for data quality assurance in CS is education and the provisioning of technical information and resources (Wiggins et al., 2011, Gillooly et al., 2019), in order to increase citizen knowledge about the issues of air pollution and to improve their environmental awareness and motivation to provide air quality monitoring supporting activities (Commodore et al., 2017, Penza et al., 2017, Kosmidis et al., 2018). Of the range of crowdsourced data quality measures discussed in the academic literature by Haklay (2010) and Foody et al. (2018), among others, attribute accuracy and completeness are essential. Furthermore, those aspects of geographic data quality have also been recognized by international standards of spatial data quality. The ISO 19157 (2013), which handles the diverse perspective of data quality, defines a set of standardized data quality measures, including completeness of data, positional accuracy, and temporal accuracy, which are all grouped as so-called data quality elements (DQEs; Fonte et al., 2017). Each DQE is, then, further evaluated and the result of the evaluation is documented and reported (Foody et al., 2018). The principles of the aforementioned ISO 19157 (2013) served as the basis for the proposed APSM data quality framework. Air pollution-related health symptoms were recorded with the use of survey questions. The survey design allowed us to select and reject attribute table contradictions, in order to reduce data bias, such as user response inconsistency, location inaccuracy, and duplicate time–space-related reports.
By combining several logic-based data quality assurance mechanisms (QAms), we tested the robustness of the QAms to find the strongest QAm set and build a ranked data quality assurance system (QAs).
To achieve the SDG 11.6 target, reliable sources of spatial data are needed. We did not solve the problem caused by the non-air pollution-related factors which affect human symptom severity, which act synergistically with air pollution to contribute to spatial database robustness on health-related symptoms (Chehregani et al., 2004, Karatzas, 2009, Sofiev and Bergmann, 2013, Bastl et al., 2015, D’Amato et al., 2015).
The goal of this study was to answer the question of quality assurance mechanism implementation in the GeoWeb-based APSM. For this purpose, we propose a dedicated air pollution symptom mapping (APSM) framework for the following QA mechanisms: start-check, sequence, cross-validation, repeating, and time-loop check (see Section 2). Our research question was: Which QAs best reduces data bias in APSM? The sources of data bias include contradictory entries in the geodatabase attribute table recorded as answers supplied to the specially created APSM survey. By answering the research question, we aim to underline the importance of CS for the achievement of the SDG 11 and 11.6 targets.