Personal Air Quality Research Articles

Associations of personal exposure to air pollutants with airway mechanics in children with asthma

BackgroundThe importance of airway mechanics has been increasingly recognized in pediatric asthma. However, no studies have examined responses of airway mechanics to air pollution exposure in asthmatic children. MethodsIn this panel study involving indoor air filtration manipulation that created a large gradient of personal exposure to PM2.5, the airway mechanics and lung function of 43 asthmatic children 5–13 years old in a suburb of Shanghai were measured four times within 3 consecutive months. Concentrations of indoor and outdoor PM2.5 and ozone were coupled with individual time-activity data to calculate personal exposures. Linear mixed effects models were used to examine the relationships of personal exposure with indicators of airway mechanics and lung function, respectively. ResultsAn interquartile range (IQR) increase in 24-hour average PM2.5 personal exposure (30.3 µg/m3) in the prior day was associated with significant increases in small airway resistance (R5-R20) of 15.8%, total airway resistance (R5) of 6.3%, and airway inflammation (FeNO) of 9.6%. These associations were stronger in children with lower blood eosinophil counts (<450/µL). No significant associations were found between personal PM2.5 exposure and lung function. Low-level ozone exposure (daily maximum 8-hour exposure range 1.1–56.4 ppb) was not significantly associated with any of the outcomes. ConclusionChanges in personal PM2.5 exposure, partly enhanced by air filtration, were associated with significant changes in airway resistance and inflammation in children with asthma. These findings suggest the importance of reducing PM2.5 exposure, via personal air quality management, in improving airflow limitation in the airways, especially the small airways.

Design, Modeling and Simulation of a Capacitive Size-Discriminating Particulate Matter Sensor for Personal Air Quality Monitoring

We applied the well-established thermophoretic effect to air quality monitoring. We developed a novel method for particulate matter distribution analysis in an in-flow capacitive detection device. The proposed Multiphysics model combines fluid dynamics of particulate matter influenced by thermophoresis with electric field variations in the active volume space of a charged coplanar interdigitated electrodes. The model allows to anticipate the effect of thermophoresis in separating particles of PM10 and PM2.5 size ranges into different streams from a single particle-entrained flow and provides an estimated value of sensitivity for capacitive PM detection. The model is described through the Finite Element Method from the main equations to the simulation run using COMSOL Multiphysics and validated by comparing the results with literature. We obtained high sensor sensitivity of up to 0.48 zF/particle as far as $18~\mu \text{m}$ from coplanar electrode surface using the Computational Fluid Dynamics and Heat Transfer, Electrostatics and Particle tracing modules. We compare results of the simulations for different particle positions, electrode width and inter-electrode spacing, then we use the results to identify optimal design parameters for a novel architecture of a PM detection system with high sensitivity down to PM2.5 single particles and embedded particle size discrimination by using $10~\mu \text{m}$ electrode width and $1\mu \text{m}$ inter-electrode spacing.

Healthier breath for all and everywhere – Urban air quality monitoring experience

Abstract Issue/problem Increasing attention is being paid to air pollution as one of the greatest threats to public and urban health. The WHO’s Urban Health Initiative points out the importance of collecting data and mapping the present state of air quality in urban areas. For citizens, such engagement is enabled by the appearance of personal air quality measurement devices that use crowd-sourcing to make measurement results publicly accessible in real time. Description of the problem As a way of contributing to air pollution monitoring in their town, three PhD Public health students conducted over 40 measurements between the start of June and end of August 2018 on various locations in the city of Novi Sad, Serbia. Measurements were performed using AirBeam personal air quality monitoring devices and their results presented as μg/m3 of Particulate Matter 2.5 (PM2.5) and automatically uploaded to the internet using the Air-casting app. Results Measurements conducted in public transportation vehicles returned the rather high average value of 40 μg/m3, where coffee shops and restaurants scored an even higher value of 48,67 μg/m3. The lowest average air pollution levels were registered near the Danube river bank (5.67) and in the parks (6), while the sites near crossroads or in the street showed average air pollution of 8.33 μg/m3. Residential areas where smoking is present during the day reported 2.5 times higher PM2.5 values than those without smokers (33.8 and 12.78 μg/m3). Lessons Bearing in mind that the air quality is considered as a serious health risk in urban areas, results of this pilot investigation suggest potential health risk for citizens living in urban areas. The negative effects of combustion and smoking on air quality are strongly highlighted, as well as the positive impact of green areas and parks near residential areas. Key messages Air pollution exposure as a serious health risk in urban areas. Crowdsourcing as a way of air quality monitoring has great potential for contributing to public health.

Discrimination of VOCs molecules via extracting concealed features from a temperature-modulated p-type NiO sensor

Poor selectivity of metal oxide semiconductor (MOS) gas sensors (toward volatile organic compounds, VOCs) poses a significant challenge for their applications in the emerging areas of personal health and air quality monitoring. Extensive efforts have been devoted to improving the selectivity of gas sensors via extracting features from their electrical response signals. Alternative to the conventional strategy of enlarging the number of sensor arrays, analyzing the transient signal of a temperature modulated gas sensor provides an efficient approach to extract molecule features. Despite p-type MOS outperforms n-type counterpart in terms of (photo)catalytic properties, further exploration on thermal modulation of p-type MOS sensor has been scarcely reported. In this work, p-type NiO nanoparticles with grain size of 17.4 ± 4.0 nm have been synthesized with the assistance of bacterial cellulose (BC) scaffold. Transient response characteristics of NiO sensor (modulated by a staircase waveform) toward 5 kinds of VOCs have been investigated. The removals of irrelevant electrical signals, particularly induced by large temperature coefficient of resistance (TCR) of p-NiO, allows us to extract the intrinsic features of tested VOCs molecules by discrete wavelet transform (DWT). Successful classification and recognition of tested VOCs molecules, including three kinds of benzene series (benzene, toluene and chlorobenzene), have been achieved by typically non-selective p-type NiO sensor with a low sensitivity. Our work highlights that eliminating the irrelevant thermally modulated electric signals is essential for expanding the recognition capability of a single MOS sensor (toward VOCs molecules), and sheds light on the exploring future smart gas molecule recognition chips.

Performance Assessment of a Low-Cost PM2.5 Sensor for a near Four-Month Period in Oslo, Norway

The very low-cost Nova particulate matter (PM) sensor SDS011 has recently drawn attention for its use for measuring PM mass concentration, which is frequently used as an indicator of air quality. However, this sensor has not been thoroughly evaluated in real-world conditions and its data quality is not well documented. In this study, three SDS011 sensors were evaluated by co-locating them at an official, air quality monitoring station equipped with reference-equivalent instrumentation in Oslo, Norway. The sensors’ measurement results for PM2.5 were compared with data generated from the air quality monitoring station over almost a four-month period. Five performance aspects of the sensors were examined: operational data coverage, linearity of response and accuracy, inter-sensor variability, dependence on relative humidity (RH) and temperature (T), and potential improvement of sensor accuracy, by data calibration using a machine-learning method. The results of the study are: (i) the three sensors provide quite similar results, with inter-sensor correlations exhibiting R values higher than 0.97; (ii) all three sensors demonstrate quite high linearity against officially measured concentrations of PM2.5, with R2 values ranging from 0.55 to 0.71; (iii) high RH (over 80%) negatively affected the sensor response; (iv) data calibration using only the RH and T recorded directly at the three sensors increased the R2 value from 0.71 to 0.80, 068 to 0.79, and 0.55 to 0.76. The results demonstrate the general feasibility of using these low cost SDS011 sensors for indicative PM2.5 monitoring under certain environmental conditions. Within these constraints, they further indicate that there is potential for deploying large networks of such devices, due to the sensors’ relative accuracy, size and cost. This opens up a wide variety of applications, such as high-resolution air quality mapping and personalized air quality information services. However, it should be noted that the sensors exhibit often very high relative errors for hourly values and that there is a high potential of abusing these types of sensors if they are applied outside the manufacturer-provided specifications particularly regarding relative humidity. Furthermore, our analysis covers only a relatively short time period and it is desirable to carry out longer-term studies covering a wider range of meteorological conditions.

Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments.

The inaccurate quantification of personal exposure to air pollution introduces error and bias in health estimations, severely limiting causal inference in epidemiological research worldwide. Rapid advancements in affordable, miniaturised air pollution sensor technologies offer the potential to address this limitation by capturing the high variability of personal exposure during daily life in large-scale studies with unprecedented spatial and temporal resolution. However, concerns remain regarding the suitability of novel sensing technologies for scientific and policy purposes. In this paper we characterise the performance of a portable personal air quality monitor (PAM) that integrates multiple miniaturised sensors for nitrogen oxides (NOx), carbon monoxide (CO), ozone (O3) and particulate matter (PM) measurements along with temperature, relative humidity, acceleration, noise and GPS sensors. Overall, the air pollution sensors showed high reproducibility (mean = 0.93, min–max: 0.80–1.00) and excellent agreement with standard instrumentation (mean = 0.82, min–max: 0.54–0.99) in outdoor, indoor and commuting microenvironments across seasons and different geographical settings. An important outcome of this study is that the error of the PAM is significantly smaller than the error introduced when estimating personal exposure based on sparsely distributed outdoor fixed monitoring stations. Hence, novel sensing technologies such as the ones demonstrated here can revolutionise health studies by providing highly resolved reliable exposure metrics at a large scale to investigate the underlying mechanisms of the effects of air pollution on health.

Assessment of personal integrated exposure to fine particulate matter of urban residents in Hong Kong

ABSTRACTMetropolitan residents are concerned about their exposure to airborne pollutants. But establishing these exposures is challenging. A compact personal exposure kit (PEK) was developed to evaluate personal integrated exposure (PIE) from time-resolved data to particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) in five microenvironments, including office, home, commuting, other indoor activities (other than home and office), and outdoor activities experienced both on weekdays and weekends. The study was conducted in Hong Kong. The PEK measured PM2.5, reported location and several other factors, stored collected data, as well as reported the data back to the investigators using global system for mobile communication (GSM) telemetry. Generally, PM2.5 concentrations in office microenvironment were found to be the smallest (13.0 μg/m3), whereas the largest PM2.5 concentration microenvironments were experienced during outdoor activities (54.4 μg/m3). Participants spent more than 85% of their time indoors, including in offices, homes, and other public indoor venues. On average, 42% and 81% of the time were spent in homes, which contributed 52% and 79% of PIE (during weekdays and weekends, respectively), suggesting that improvement of air quality in homes may reduce overall exposures and indicating the need for actions to mitigate possible public health burdens in Hong Kong. This study also found that various indoor/outdoor microenvironments experienced by urban office workers cannot be accurately represented by general urban air quality data reported from the regulatory monitoring. Such personalized air quality information, especially while in transit or in offices and homes, may provide improved information on population exposures to air pollution.Implications: A newly developed personal exposure kit (PEK) was used to monitor PM2.5 exposure of metropolitan citizens in their daily life. Different microenvironments and time durations caused various personal integrated exposure (PIE). The stationary monitoring method for PIE was also compared and evaluated with PEK. Positive protection actions can be taken after understanding the major contribution to PM2.5 exposure.

Mobile monitoring of air pollution using low cost sensors to visualize spatio-temporal variation of pollutants at urban hotspots

This paper deals with the development of a smart personal air quality mon- itoring system (SPAMS) for real time air quality monitoring. The commer- cially available low-cost CO, NO2, O3, PM, temperature (T) and humidity (RH) sensors along with the microcontroller and GPRS were integrated in SPAMS. The developed SPAMS was calibrated in the laboratory and vali- dated through field measurements. Field measurements were performed by walking on footpaths and travelling in the bus during various times of a day and different days in a week at selected locations in Chennai city, India. Re- sults indicated that the PM2.5 concentration was highest in busy traffic site (mean = 22.7 ± 8.45 μg/m3) and lowest in urban background (mean = 9.3 ± 5.75 μg/m3). Whereas, the CO concentration was lowest at beach road (mean = 0.26 ± 0.01 ppm). Not much variation was observed in the average concentrations of NO2 and O3. On the other hand, while travelling in buses, higher concentration of CO and NO2 were observed during the morning and evening periods due to the peaking traffic flow. On contrary O3 concentration was found to be highest during afternoon due to photochemical reactions.

Preliminary results from the COPE study using primary-care electronic health records and environmental modelling to examine COPD exacerbations

BackgroundChronic Obstructive Airway Disease (COPD) is marked by often severely debilitating exacerbations. Efficient patient-centric research approaches are needed to better inform health management primary-care.AimThe ‘COPE study’ aims to develop a method of predicting COPD exacerbations utilising personal air quality sensors, environmental exposure modelling and electronic health records through the recruitment of patients from consenting GPs contributing to the Clinical Practice Research Datalink (CPRD).MethodThe study made use of Electronic Healthcare Records (EHR) from CPRD, an anonymised GP records database to screen and locate patients within GP practices in Central London. Personal air monitors were used to capture data on individual activities and environmental exposures. Output from the monitors were then linked with the EHR data to obtain information on COPD management, severity, comorbidities and exacerbations. Symptom changes not equating to full exacerbations were captured on diary cards. Linear regression was used to investigate the relationship between subject peak flow, symptoms, exacerbation events and exposure data.ResultsPreliminary results on the first 80 patients who have completed the study indicate variable susceptibility to environmental stressors in COPD patients. Some individuals appear highly susceptible to environmental stress and others appear to have unrelated triggers.ConclusionRecruiting patients through EHR for a study is feasible and allows easy collection of data for long term follow up. Portable environmental sensors could now be used to develop personalised models to predict risk of COPD exacerbations in susceptible individuals. Identification of direct links between participant health and activities would allow improved health management thus cost savings.

Visualising and evaluating objective citizen-contributed environmental information: the CITI-SENSE Citizens’ Observatory Toolbox

Introduction: The Citizens’ Observatory Toolbox (COT) has been developed within the CITI-SENSE project (partly funded by the EU FP7-ENV-2012, grant agreement no. 308524) for collecting citizen-contributed observations of air quality. Methods: Environmental micro-sensor nodes for measuring air quality have been developed and are used to monitor NO, NO2, O3, CO, PM2.5, PM10, and noise. Static nodes (AQMesh pods) and portable nodes were deployed with different stakeholders. The portable nodes are coupled to the ExpoApp smartphone application (Android), which also collects geolocation and accelerometry data. Both the AQMesh pods and the LEOs automatically and wirelessly transmit encrypted data to dedicated Spatial and Environmental Data Service (SEDS) and Web Feature Service (WFS) databases, where they are processed for visualisation and evaluation for usefulness by users. Results: AQMesh pods with an updated algorithm (v4.1) show highest correlations (R2 = 0.76–0.81) for NO2 when comparing 15-minute averages obtained by reference instruments at municipal monitoring stations. The portable nodes, due to the challenges of micro-sensors and egomotion, are communicating real-time Air Pollution Indication (APIN) levels to users. The APIN is based upon the Common Air Quality Index (CAQI) but represents minute averages, showing potential to improve micro-environmental exposure classifications as well as personal air quality advice. Dynamic city-wide air quality maps have been produced using data assimilation techniques, fusing data from the AQMesh pods with base maps obtained by land-use regression modeling. The products are being evaluated by focus groups of stakeholders, to assess the usefulness of the COT. Conclusions: The CITI-SENSE COT is facilitating environmental health governance and contributing to future work on time-space-activity exposure classification and dynamic air quality maps. All tools within it are being made available online upon completion of the project.

Linking e-health records, patient-reported symptoms and environmental exposure data to characterise and model COPD exacerbations: protocol for the COPE study.

IntroductionRelationships between exacerbations of chronic obstructive pulmonary disease (COPD) and environmental factors such as temperature, humidity and air pollution are not well characterised, due in part to oversimplification in the assignment of exposure estimates to individuals and populations. New developments in miniature environmental sensors mean that patients can now carry a personal air quality monitor for long periods of time as they go about their daily lives. This creates the potential for capturing a direct link between individual activities, environmental exposures and the health of patients with COPD. Direct associations then have the potential to be scaled up to population levels and tested using advanced human exposure models linked to electronic health records.Methods and analysisThis study has 5 stages: (1) development and deployment of personal air monitors; (2) recruitment and monitoring of a cohort of 160 patients with COPD for up to 6 months with recruitment of participants through the Clinical Practice Research Datalink (CPRD); (3) statistical associations between personal exposure with COPD-related health outcomes; (4) validation of a time-activity exposure model and (5) development of a COPD prediction model for London.Ethics and disseminationThe Research Ethics Committee for Camden and Islington has provided ethical approval for the conduct of the study. Approval has also been granted by National Health Service (NHS) Research and Development and the Independent Scientific Advisory Committee. The results of the study will be disseminated through appropriate conference presentations and peer-reviewed journals.

CMOS Amperometric ADC With High Sensitivity, Dynamic Range and Power Efficiency for Air Quality Monitoring.

Airborne pollutants are a leading cause of illness and mortality globally. Electrochemical gas sensors show great promise for personal air quality monitoring to address this worldwide health crisis. However, implementing miniaturized arrays of such sensors demands high performance instrumentation circuits that simultaneously meet challenging power, area, sensitivity, noise and dynamic range goals. This paper presents a new multi-channel CMOS amperometric ADC featuring pixel-level architecture for gas sensor arrays. The circuit combines digital modulation of input currents and an incremental Σ∆ ADC to achieve wide dynamic range and high sensitivity with very high power efficiency and compact size. Fabricated in 0.5 [Formula: see text] CMOS, the circuit was measured to have 164 dB cross-scale dynamic range, 100 fA sensitivity while consuming only 241 [Formula: see text] and 0.157 [Formula: see text] active area per channel. Electrochemical experiments with liquid and gas targets demonstrate the circuit's real-time response to a wide range of analyte concentrations.

Stability and Control of a Metal Oxide Gas Sensor in Simulated Wind

Semiconducting metal oxide gas chemical sensors show promise for applications in handheld and personalized air quality monitoring systems. One limitation of these sensors is their cross-response to outdoor factors, such as temperature and humidity fluctuations. Previously, airflow (e.g., wind) was known to modify sensor output, but the cause of this response was not greatly characterized. In this paper, we investigate the effect of airflow on the output of a commercial metal oxide sensor in dry and humid air. Upon exposure to a step in airflow, the output of these sensors increases by more than 6% over 1 h. We also control the temperature of the sensor’s embedded heater resistor using an external proportional–integral controller. With this control, the sensor output after exposure to airflow increases by <1% over 1 h, demonstrating a stabilization scheme for air quality measurements that does not require additional temperature sensing equipment.

MEMS/Nano-technologies for Smart Air Environmental Monitoring Sensors

The importance of air quality monitoring is rapidly increasing. Even though state-of-the-art air quality monitoring technologies such as mass spectrometry, gas chromatography, and optical measurement enable high-fidelity measurement of air pollutants, they cannot be widely used for portable or personalized platforms because of their high cost and complexity. Recently, personalized and localized environmental monitoring, rather than global and averaged environmental monitoring, has drawn greater attention with the advancement of mobile telecommunication technologies. Here, micro- and nano-technologies enable highly integrated and ultra-compact sensors to meet the needs of personalized environmental monitoring. In this paper, several examples of MEMS-based gas sensors for compact and personalized air quality monitoring are explained. Additionally, the principles and usage of functional nanomaterials are discussed for highly sensitive and selective gas sensors.

Examining Personal Air Pollution Exposure, Intake, and Health Danger Zone Using Time Geography and 3D Geovisualization

Expanding traditional time geography, this study examines personal exposure to air pollution and personal pollutant intake, and defines personal health danger zones by accounting for individual level space-time behavior. A 3D personal air pollution and health risk map is constructed to visualize individual space-time path, personal Air Quality Indexes (AQIs), and personal health danger zones. Personal air pollution exposure level and its variation through space and time is measured by a portable air pollutant sensor coupled with a portable GPS unit. Personal pollutant intake is estimated by accounting for air pollutant concentration in immediate surroundings, individual’s biophysical characteristics, and individual’s space-time activities. Personal air pollution danger zones are defined by comparing personal pollutant intake with air quality standard; these zones are particular space-time-activity segments along an individual’s space-time path. Being able to identify personal air pollution danger zones can help plan for proper actions aiming at controlling health impacts from air pollution. As a case study, this paper reports on an examination and visualization of an individual’s two-day ozone exposure, intake and danger zones in Houston, Texas.

The next generation of low-cost personal air quality sensors for quantitative exposure monitoring

Abstract. Advances in embedded systems and low-cost gas sensors are enabling a new wave of low-cost air quality monitoring tools. Our team has been engaged in the development of low-cost, wearable, air quality monitors (M-Pods) using the Arduino platform. These M-Pods house two types of sensors – commercially available metal oxide semiconductor (MOx) sensors used to measure CO, O3, NO2, and total VOCs, and NDIR sensors used to measure CO2. The MOx sensors are low in cost and show high sensitivity near ambient levels; however they display non-linear output signals and have cross-sensitivity effects. Thus, a quantification system was developed to convert the MOx sensor signals into concentrations. We conducted two types of validation studies – first, deployments at a regulatory monitoring station in Denver, Colorado, and second, a user study. In the two deployments (at the regulatory monitoring station), M-Pod concentrations were determined using collocation calibrations and laboratory calibration techniques. M-Pods were placed near regulatory monitors to derive calibration function coefficients using the regulatory monitors as the standard. The form of the calibration function was derived based on laboratory experiments. We discuss various techniques used to estimate measurement uncertainties. The deployments revealed that collocation calibrations provide more accurate concentration estimates than laboratory calibrations. During collocation calibrations, median standard errors ranged between 4.0–6.1 ppb for O3, 6.4–8.4 ppb for NO2, 0.28–0.44 ppm for CO, and 16.8 ppm for CO2. Median signal to noise (S / N) ratios for the M-Pod sensors were higher than the regulatory instruments: for NO2, 3.6 compared to 23.4; for O3, 1.4 compared to 1.6; for CO, 1.1 compared to 10.0; and for CO2, 42.2 compared to 300–500. By contrast, lab calibrations added bias and made it difficult to cover the necessary range of environmental conditions to obtain a good calibration. A separate user study was also conducted to assess uncertainty estimates and sensor variability. In this study, 9 M-Pods were calibrated via collocation multiple times over 4 weeks, and sensor drift was analyzed, with the result being a calibration function that included baseline drift. Three pairs of M-Pods were deployed, while users individually carried the other three. The user study suggested that inter-M-Pod variability between paired units was on the same order as calibration uncertainty; however, it is difficult to make conclusions about the actual personal exposure levels due to the level of user engagement. The user study provided real-world sensor drift data, showing limited CO drift (under −0.05 ppm day−1), and higher for O3 (−2.6 to 2.0 ppb day−1), NO2 (−1.56 to 0.51 ppb day−1), and CO2 (−4.2 to 3.1 ppm day−1). Overall, the user study confirmed the utility of the M-Pod as a low-cost tool to assess personal exposure.

Capacitive detection of micrometric airborne particulate matter for solid-state personal air quality monitors

A novel microsensor for capacitive detection of particulate matter (PM) directly in air is presented. The feasibility of detecting single PM10 particles (calibrated 10μm polystyrene beads) by means of ad-hoc designed coplanar microelectrodes (4μm gap), a PDMS air deposition system, and a low-noise (∼2aF capacitive resolution) readout electronics is experimentally demonstrated. Finite element numerical simulations have been performed to optimize the design of the microelectrodes, investigate the detection limit and validate the experimental results. The real-time deposition on the sensor surface of sequences of single industrial talc particles (average diameter of ∼8μm, corresponding to a signal of ∼12aF) has been successfully tracked with 10ms temporal resolution and, subsequently, validated by microscope inspection. This CMOS- and MEMS-compatible capacitance detection technique enables radical miniaturization of next generation air quality monitors, paving the ways to their embedment in personal portable devices for pervasive mapping of air pollution.

Toward the next generation of air quality monitoring: Particulate Matter

Fine particulate matter is one of the key global pollutants affecting human health. Satellite and ground-based monitoring technologies as well as chemical transport models have advanced significantly in the past 50 years, enabling improved understanding of the sources of fine particles, their chemical composition, and their effect on human and environmental health. The ability of air pollution to travel across country and geographic boundaries makes particulate matter a global problem. However, the variability in monitoring technologies and programs and poor data availability make global comparison difficult. This paper summarizes fine particle monitoring, models that integrate ground-based and satellite-based data, and communications, then recommends steps for policymakers and scientists to take to expand and improve local and global indicators of particulate matter air pollution. One of the key set of recommendations to improving global indicators is to improve data collection by basing particulate matter monitoring design and stakeholder communications on the individual country, its priorities, and its level of development, while at the same time creating global data standards for inter-country comparisons. When there are good national networks that produce consistent quality data that is shared openly, they serve as the foundation for better global understanding through data analysis, modeling, health impact studies, and communication. Additionally, new technologies and systems should be developed to expand personal air quality monitoring and participation of non-specialists in crowd-sourced data collections. Finally, support to the development and improvement of global multi-pollutant indicators of the health and economic effects of air pollution is essential to addressing improvement of air quality around the world.

Impact of Different Household Fuel Use on Source Apportionment Results of House-Indoor RPM in Central India

Conventional character of household fuel use in India showed significant impact on residential-indoor air quality and consequently deteriorating personal air quality of inhabitants. Multi-complexity in location and type of industrial units along with haphazard traffic system are some of the major challenges in resolving relative source contribution with higher significance, especially in case of indoor and personal level air quality. On the basis of questionnaire survey that include daily activity pattern with use of household cooking fuel by local residents, three fuel categories identified: 1) Use of Liquid Petroleum Gas (LPG) stove, 2) Use of kerosene stoves and electric stoves and 3) Conventional stoves using coke and cow dung cakes. In many of middle class residents, kitchen cooking is depending on all three categorized fuels. A longitudinal measurements of respirable particulate matter (RPM) in residential-indoors, ambient-outdoors of selected source sites and local-outdoor of road-traffic junctions have been monitored during October–December 2009. Sampling frequency was ten (twice in a week). Chemical Mass Balance (CMB8) was executed using source-routes and indoor-receptor compositional profiles. Results have shown significant variation in relative contributions of outdoor sources with potential impact of household fuel emission on source contributions of indoor RPM. Results also explained extent of contribution from road-traffic emissions, local soils and major industrial complex to indoor RPM levels. RELEVANCE: The study is the part of comprehensive assessment of source apportionment of particulate matter at ambient, indoor, personal and surface levels in urban-industrial environment of central India using. 21 chemical species have been analyzed using standard reported protocol of ICP-AES and ion-chromatograph. Source apportionment has been conducted using two methods: Preliminary- linear regression, then using CMB8.

Pilot study results for a novel system of networked, low cost personal air quality monitors

Pilot study results for a novel system of networked, low cost personal air quality monitors