Air Quality Sensors Research Articles

Laboratory Chamber Evaluation of Flow Air Quality Sensor PM2.5 and PM10 Measurements.

The emergence of low-cost air quality sensors as viable tools for the monitoring of air quality at population and individual levels necessitates the evaluation of these instruments. The Flow air quality tracker, a product of Plume Labs, is one such sensor. To evaluate these sensors, we assessed 34 of them in a controlled laboratory setting by exposing them to PM10 and PM2.5 and compared the response with Plantower A003 measurements. The overall coefficient of determination (R2) of measured PM2.5 was 0.76 and of PM10 it was 0.73, but the Flows’ accuracy improved after each introduction of incense. Overall, these findings suggest that the Flow can be a useful air quality monitoring tool in air pollution areas with higher concentrations, when incorporated into other monitoring frameworks and when used in aggregate. The broader environmental implications of this work are that it is possible for individuals and groups to monitor their individual exposure to particulate matter pollution.

Air Quality Sensor Networks for Evidence-Based Policy Making: Best Practices for Actionable Insights

(1) Background: This work evaluated the usability of commercial “low-cost” air quality sensor systems to substantiate evidence-based policy making. (2) Methods: Two commercially available sensor systems (Airly, Kunak) were benchmarked at a regulatory air quality monitoring station (AQMS) and subsequently deployed in Kampenhout and Sint-Niklaas (Belgium) to address real-world policy concerns: (a) what is the pollution contribution from road traffic near a school and at a central city square and (b) do local traffic interventions result in quantifiable air quality impacts? (3) Results: The considered sensor systems performed well in terms of data capture, correlation and intra-sensor uncertainty. Their accuracy was improved via local re-calibration, up to data quality levels for indicative measurements as set in the Air Quality Directive (Uexp < 50% for PM and <25% for NO2). A methodological setup was proposed using local background and source locations, allowing for quantification of the (3.1) maximum potential impact of local policy interventions and (3.2) air quality impacts from different traffic interventions with local contribution reductions of up to 89% for NO2 and 60% for NO throughout the considered 3 month monitoring period; (4) Conclusions: Our results indicate that commercial air quality sensor systems are able to accurately quantify air quality impacts from (even short-lived) local traffic measures and contribute to evidence-based policy making under the condition of a proper methodological setup (background normalization) and data quality (recurrent calibration) procedure. The applied methodology and learnings were distilled in a blueprint for air quality sensor networks for replication actions in other cities.

Performance characterization of low-cost air quality sensors for off-grid deployment in rural Malawi

Abstract. Low-cost gas and particulate matter sensor packages offer a compact, lightweight, and easily transportable solution to address global gaps in air quality (AQ) observations. However, regions that would benefit most from widespread deployment of low-cost AQ monitors often lack the reference-grade equipment required to reliably calibrate and validate them. In this study, we explore approaches to calibrating and validating three integrated sensor packages before a 1-year deployment to rural Malawi using colocation data collected at a regulatory site in North Carolina, USA. We compare the performance of five computational modeling approaches to calibrate the electrochemical gas sensors: k-nearest neighbors (kNN) hybrid, random forest (RF) hybrid, high-dimensional model representation (HDMR), multilinear regression (MLR), and quadratic regression (QR). For the CO, Ox, NO, and NO2 sensors, we found that kNN hybrid models returned the highest coefficients of determination and lowest error metrics when validated. Hybrid models were also the most transferable approach when applied to deployment data collected in Malawi. We compared kNN hybrid calibrated CO observations from two regions in Malawi to remote sensing data and found qualitative agreement in spatial and annual trends. However, ARISense monthly mean surface observations were 2 to 4 times higher than the remote sensing data, partly due to proximity to residential biomass combustion activity not resolved by satellite imaging. We also compared the performance of the integrated Alphasense OPC-N2 optical particle counter to a filter-corrected nephelometer using colocation data collected at one of our deployment sites in Malawi. We found the performance of the OPC-N2 varied widely with environmental conditions, with the worst performance associated with high relative humidity (RH >70 %) conditions and influence from emissions from nearby residential biomass combustion. We did not find obvious evidence of systematic sensor performance decay after the 1-year deployment to Malawi. Data recovery (30 %–80 %) varied by sensor and season and was limited by insufficient power and access to resources at the remote deployment sites. Future low-cost sensor deployments to rural, low-income settings would benefit from adaptable power systems, standardized sensor calibration methodologies, and increased regional regulatory-grade monitoring infrastructure.

Development of a New Wearable 3D Sensor Node and Innovative Open Classification System for Dairy Cows' Behavior.

Simple SummaryIn order to keep dairy cows under satisfactory health and welfare conditions, it is very important to monitor the animals in their living environment. With the support of technology, and, in particular, with the installation of sensors on neck-collars, cow behavior can be adequately monitored, and different behavioral patterns can be classified. In this study, an open and customizable device has been developed to classify the behaviors of dairy cows. The device communicates with a mobile application via Bluetooth to acquire raw data from behavioral observations and via an ad hoc radio channel to send the data from the device to the gateway. After observing 32 cows on 3 farms for a total of 108 h, several machine learning algorithms were trained to classify their behaviors. The decision tree algorithm was found to be the best compromise between complexity and accuracy to classify standing, lying, eating, and ruminating. The open nature of the system enables the addition of other functions (e.g., localization) and the integration with other information sources, e.g., climatic sensors, to provide a more complete picture of cow health and welfare in the barn.Monitoring dairy cattle behavior can improve the detection of health and welfare issues for early interventions. Often commercial sensors do not provide researchers with sufficient raw and open data; therefore, the aim of this study was to develop an open and customizable system to classify cattle behaviors. A 3D accelerometer device and host-board (i.e., sensor node) were embedded in a case and fixed on a dairy cow collar. It was developed to work in two modes: (1) acquisition mode, where a mobile application supported the raw data collection during observations; and (2) operating mode, where data was processed and sent to a gateway and on the cloud. Accelerations were sampled at 25 Hz and behaviors were classified in 10-min windows. Several algorithms were trained with the 108 h of behavioral data acquired from 32 cows on 3 farms, and after evaluating their computational/memory complexity and accuracy, the Decision Tree algorithm was selected. This model detected standing, lying, eating, and ruminating with an average accuracy of 85.12%. The open nature of this system enables for the addition of other functions (e.g., real-time localization of cows) and the integration with other information sources, e.g., microenvironment and air quality sensors, thereby enhancing data processing potential.

Machine learning techniques to improve the field performance of low-cost air quality sensors

Abstract. Low-cost air quality sensors offer significant potential for enhancing urban air quality networks by providing higher-spatiotemporal-resolution data needed, for example, for evaluation of air quality interventions. However, these sensors present methodological and deployment challenges which have historically limited operational ability. These include variability in performance characteristics and sensitivity to environmental conditions. In this work, we investigate field “baselining” and interference correction using random forest regression methods for low-cost sensing of NO2, PM10 (particulate matter) and PM2.5. Model performance is explored using data obtained over a 7-month period by real-world field sensor deployment alongside reference method instrumentation. Workflows and processes developed are shown to be effective in normalising variable sensor baseline offsets and reducing uncertainty in sensor response arising from environmental interferences. We demonstrate improvements of between 37 % and 94 % in the mean absolute error term of fully corrected sensor datasets; this is equivalent to performance within ±2.6 ppb of the reference method for NO2, ±4.4 µg m−3 for PM10 and ±2.7 µg m−3 for PM2.5. Expanded-uncertainty estimates for PM10 and PM2.5 correction models are shown to meet performance criteria recommended by European air quality legislation, whilst that of the NO2 correction model was found to be narrowly (∼5 %) outside of its acceptance envelope. Expanded-uncertainty estimates for corrected sensor datasets not used in model training were 29 %, 21 % and 27 % for NO2, PM10 and PM2.5 respectively.

Pengiriman Data Surveillance Sensing Menggunakan Sistem Nirkabel Pada Frekuensi 433 MHz Untuk Kebakaran Hutan

The fires that hit Indonesia in 2015 are considered one of the biggest environmental disasters of the 21st century so far. The World Bank estimates that the 2015 fire crisis cost Indonesia US$16 billion in forestry, agriculture, tourism and other industries. The smog causes respiratory and other illnesses in hundreds of thousands of people across the region. So far, efforts to mitigate forest and land fires have often been carried out by the government and the forest fire task force, starting from creating an early warning system, increasing community participation, and forest fire control techniques. However, all of these mitigation efforts are more dominantly carried out after forest and land fires have occurred. Whereas when a fire has occurred and the fire has grown bigger, fire mitigation efforts become more difficult and expensive because they have to involve helicopters to extinguish the fire from the air. So a land and fire mitigation effort requires one that is easy to use, fast and cheap to implement, and can reach a wider area than a forest area. The research aims to create a surveillance sensing system for forest fire for forest fire mitigation based on drone technology, and sensor technology. This research is an innovation and development of science and technology that is beneficial to the community, the forest fire task force and the local government. The research stages start from the design, manufacture, and design of a surveillance sensing system for forest fire in the Telecommunication Network laboratory. The output of this research is a prototype surveillance sensing system for forest fire. Test results within a certain time span indicate that the sensor is running well. All sensors used will show a change in value when a heat source and fire are detected. The average temperature sensor result is 72.34 C. While the dust particle sensor result is 665.45 g/m3 . And for air quality sensors the average is 0.225 ppm. For GPS sensor data it has 99.5% accuracy. which the Arduino reads before sending. The maximum delivery distance that can be used is 0-40 meters.

Adaptive Air Quality Sensing using Machine Learning

Abstract: Air pollution is a worldwide problem having impacts on both local and global scales. According to the World Health Organization (WHO), air pollution causes 7 million deaths every year, with 4.2 million attributed to exposure to outdoor air pollution. Compared to the reference methods defined in the Air Quality Directive, the use of low-cost air quality sensors for monitoring ambient air pollution would reduce air pollution monitoring costs and would also allow larger spatial coverage especially in remote areas where monitoring with traditional facilities is uneasy. Theses multi-sensors were either calibrated against standard gas mixtures or using artificial neural network under field conditions. The later method resulted in mixed results either satisfactory for short periods or generally weak for longer data series. This project mainly focuses on the adaptive calibration of the low-cost sensors using the trained machine learning model. The performances of several field calibration methods for low-cost sensors, including linear/multi linear regression and supervised learning techniques will be compared. A cluster of ozone, nitrogen dioxide, nitrogen monoxide, carbon monoxide and carbon dioxide sensors will be operated. Subsequently, the accuracy of the predicted values will be evaluated for about a period. These predicted values are fed through the training model and the model will run accordingly and it will adapt all the error situations. This will be useful to the automobile industry in the current situation for smoke emission control and also for many refinery industries in which low-cost sensors can be used with the following modelling for the higher accuracy. This would reduce the cost and also yields more accuracy beyond the varying situation. Keywords: 1) Low-Cost Sensors (LCS) 2) Internet of Things (IoT)

Evaluation of Cairpol and Aeroqual Air Sensors in Biomass Burning Plumes

Cairpol and Aeroqual air quality sensors measuring CO, CO2, NO2, and other species were tested in fresh biomass burning plumes in field and laboratory environments. We evaluated sensors by comparing 1-minute sensor measurements to collocated reference instrument measurements. Sensors were evaluated based on the coefficient of determination (r 2) between the sensor and reference measurements, by the accuracy, collocated precision, root mean square error (RMSE), and other metrics. In general, CO and CO2 sensors performed well (in terms of accuracy and r 2 values) compared to NO2 sensors. Cairpol CO and NO2 sensors had better sensor-versus-sensor agreement (e.g., collocated precision) than Aeroqual CO and NO2 sensors of the same species. Tests of other sensors (e.g., NH3, H2S, VOC, NMHC) provided more inconsistent results and need further study. Aeroqual NO2 sensors had an apparent O3 interference that was not observed in the Cairpol NO2 sensors. Although the sensor accuracy lags that of reference-level monitors, with location-specific calibrations they have the potential to provide useful data about community air quality and personal exposure to smoke impacts.

Optimizing for Equity: Sensor Coverage, Networks, and the Responsive City

Decisions about sensor placement in cities are inherently complex, balancing social-technical, digital, and structural inequalities with the differential needs of populations, local stakeholder priorities, and the technical specificities of the sensors themselves. Rapid developments in urban data collection and geographic data science have the potential to support these decision-making processes. Focusing on a case study of air-quality sensors in Newcastle-upon-Tyne, UK, we employ spatial optimization algorithms as a descriptive tool to illustrate the complex trade-offs that produce sensor networks that miss important groups—even when the explicit coverage goal is one of equity. We show that the problem is not technical; rather, it is demographic, structural, and financial. Despite the considerable constraints that emerge from our analysis, we argue the data collected via sensor networks are of continued importance when evidencing core urban injustices (e.g., air pollution or climate-related heat). We therefore make the case for a clearer distinction to be made between sensors for monitoring and sensors for surveillance, arguing that a wider presumption of bad intent for all sensors potentially limits the visibility of positive types of sensing. For the purpose of monitoring, we also propose that basic spatial optimization tools can help to elucidate and remediate spatial injustices in sensor networks.

Use of Low-Cost Sensors to Characterize Occupational Exposure to PM2.5 Concentrations Inside an Industrial Facility in Santa Ana, CA: Results from a Worker- and Community-Led Pilot Study

PM2.5 is an air contaminant that has been widely associated with adverse respiratory and cardiovascular health, leading to increased hospital admissions and mortality. Following concerns reported by workers at an industrial facility located in Santa Ana, California, workers and community leaders collaborated with experts in the development of an air monitoring pilot study to measure PM2.5 concentrations to which employees and local residents are exposed during factory operating hours. To detect PM2.5, participants wore government-validated AtmoTube Pro personal air monitoring devices during three separate workdays (5 AM–1:30 PM) in August 2021. Results demonstrated a mean PM2.5 level inside the facility of 112.3 µg/m3, nearly seven-times greater than outdoors (17.3 µg/m3). Of the eight workers who wore personal indoor sampling devices, five showed measurements over 100 μg/m3. Welding-related activity inside the facility resulted in the greatest PM2.5 concentrations. This study demonstrates the utility of using low-cost air quality sensors combined with employee knowledge and participation for the investigation of workplace air pollution exposure as well as facilitation of greater health-related awareness, education, and empowerment among workers and community members. Results also underscore the need for basic measures of indoor air pollution control paired with ongoing air monitoring within the Santa Ana facility, and the importance of future air monitoring studies aimed at industrial facilities.

JUSTIFICATION OF THE STRUCTURAL AND TECHNOLOGICAL PARAMETERS OF THE ADAPTIVE THREE-PIPE HEAT EXCHANGER FOR ANIMAL ROOM

As a result of the research, an adaptive three-pipe heat exchanger was developed, containing coaxially installed pipes, a condensate drain pipe, an exhaust shaft passing through the outer pipe, a supply fan, an exhaust fan, distribution openings, an air filter, and a duct electric heater. The adaptive three-pipe heat exchanger is additionally equipped with servo drives with dampers, temperature sensors, air velocity sensors installed on all distribution openings, an air quality sensor located in the room, a control unit that is connected to the supply fan, exhaust valve by means of signal wires. heater, servo drives, temperature sensors, air speed sensors and air quality sensor As a result of theoretical studies, confirmed experimentally (correlation coefficient R = 0.98), a method was developed and based on it an algorithm for determining the area of holes in the air duct of an adaptive three-pipe heat exchanger for livestock buildings was developed . It has been established that the distance between the holes gradually decreases to a certain value in the opposite direction of the air flow. However, there is a slight reduction in distance at the end of the heat exchanger duct due to the backflow of air that collides with the plugged end.

Laser ablation assisted micropattern screen printed transduction electrodes for sensing applications

In this work we present a facile method for the fabrication of several capacitive transduction electrodes for sensing applications. To prepare the electrodes, line widths up to 300 upmum were produced on polymethyl methacrylate (PMMA) substrate using a common workshop laser engraving machine. The geometries prepared with the laser ablation process were characterised by optical microscopy for consistency and accuracy. Later, the geometries were coated with functional polymer porous cellulose decorated sensing layer for humidity sensing. The resulting sensors were tested at various relative humidity (RH) levels. In general, good sensing response was produced by the sensors with sensitivities ranging from 0.13 to 2.37 pF/%RH. In ambient conditions the response time of 10 s was noticed for all the fabricated sensors. Moreover, experimental results show that the sensitivity of the fabricated sensors depends highly on the geometry and by changing the electrode geometry sensitivity increases up to 5 times can be achieved with the same sensing layer. The simplicity of the fabrication process and higher sensitivity resulting from the electrode designs is expected to enable the application of the proposed electrodes not only in air quality sensors but also in many other areas such as touch or tactile sensors.

Air Quality Detecting Sensors

The current ongoing outbreak have taught us how a pandemic can harm public health and environments. Any attempts or improvement to make Air quality should be made affordable and viable with technological improvements. With the low-cost air quality sensors and technological advancements, need to build a system which are important to measure and study pollution that can harm public health. Now many countries have installed Air Quality sensors as it is affordable and best in use. As they are used to monitor hazardous gases, chemical pollutants, ground level ozone, Carbon monoxide, Sulphur dioxide, Nitrogen dioxide, temperature, humidity level to derive Air Quality Index. Air pollutants are the current debatable topics worldwide emerged.

Opinion: Insights into updating Ambient Air Quality Directive 2008/50/EC

Abstract. As evidence of adverse health effects due to air pollution continues to increase, the World Health Organization (WHO) recently published its latest edition of the global air quality guidelines (World Health Organization, 2021). Although not legally binding, the guidelines aim to provide a framework in which policymakers can combat air pollution by formulating evidence-based air quality management strategies. In the light of this, the European Union has stated its intent to revise the current ambient air quality directive (2008/50/EC) to more closely resemble the newly published WHO guidelines (European Commission, 2020). This article provides an informed opinion on selected features of the air quality directive that we believe would benefit from a reassessment. The selected features include discussion about (1) air quality sensors as a part of a hierarchical observation network, (2) the number of minimum sampling points and their siting criteria, and (3) new target air pollution parameters for future consideration.

Investigating the Sources of Urban Air Pollution Using Low-Cost Air Quality Sensors at an Urban Atlanta Site.

Advances in low-cost sensors (LCS) for monitoring air quality have opened new opportunities to characterize air quality in finer spatial and temporal resolutions. In this study, we deployed LCS that measure both gas (CO, NO, NO2, and O3) and particle concentrations and co-located research-grade instruments in Atlanta, GA, to investigate the capability of LCS in resolving air pollutant sources using non-negative matrix factorization (NMF) in a moderately polluted urban area. We provide a comparison of applying the NMF technique to both normalized and non-normalized data sets. We identify four factors with different temporal trends and properties for both normalized and non-normalized data sets. Both normalized and non-normalized LCS data sets can resolve primary organic aerosol (POA) factors identified from research-grade instruments. However, applying normalization provides factors with more diverse compositions and can resolve secondary organic aerosol (SOA). Results from this study demonstrate that LCS not only can be used to provide basic mass concentration information but also can be used for in-depth source apportionment studies even in an urban setting with complex pollution mixtures and relatively low aerosol loadings.

Impact of COVID-19 lockdown on PM concentrations in an Italian Northern City: A year-by-year assessment.

In the last century, the increase in traffic, human activities and industrial production have led to a diffuse presence of air pollution, which causes an increase of risk of several health conditions such as respiratory diseases. In Europe, air pollution is a serious concern that affects several areas, one of the worst ones being northern Italy, and in particular the Po Valley, an area characterized by low air quality due to a combination of high population density, industrial activity, geographical factors and weather conditions. Public health authorities and local administrations are aware of this problem, and periodically intervene with temporary traffic limitations and other regulations, often insufficient to solve the problem. In February 2020, this area was the first in Europe to be severely hit by the SARS-CoV-2 virus causing the COVID-19 disease, to which the Italian government reacted with the establishment of a drastic lockdown. This situation created the condition to study how significant is the impact of car traffic and industrial activity on the pollution in the area, as these factors were strongly reduced during the lockdown. Differently from some areas in the world, a drastic decrease in pollution measured in terms of particulate matter (PM) was not observed in the Po Valley during the lockdown, suggesting that several external factors can play a role in determining the severity of pollution. In this study, we report the case study of the city of Pavia, where data coming from 23 air quality sensors were analyzed to compare the levels measured during the lockdown with the ones coming from the same period in 2019. Our results show that, on a global scale, there was a statistically significant reduction in terms of PM levels taking into account meteorological variables that can influence pollution such as wind, temperature, humidity, rain and solar radiation. Differences can be noticed analyzing daily pollution trends too, as-compared to the study period in 2019-during the study period in 2020 pollution was higher in the morning and lower in the remaining hours.

Towards the Development of a Sensor Educational Toolkit to Support Community and Citizen Science.

As air quality sensors increasingly become commercially available, a deeper consideration of their usability and usefulness is needed to ensure effective application by the public. Much of the research related to sensors has focused on data quality and potential applications. While this information is important, a greater understanding of users’ experience with sensors would provide complementary information. Under a U.S. EPA-funded Science to Achieve Results grant awarded to the South Coast Air Quality Management District in California, titled “Engage, Educate, and Empower California Communities on the Use and Applications of Low-Cost Air Monitoring Sensors”, approximately 400 air quality sensors were deployed with 14 California communities. These communities received sensors and training, and they participated in workshops. Widely varying levels of sensor installation and engagement were observed across the 14 communities. However, despite differences between communities (in terms of participation, demographics, and socioeconomic factors), many participants offered similar feedback on the barriers to sensor use and strategies leading to successful sensor use. Here, we assess sensor use and participant feedback, as well as discuss the development of an educational toolkit titled “Community in Action: A Comprehensive Toolkit on Air Quality Sensors”. This toolkit can be leveraged by future community and citizen science projects to develop networks designed to collect air quality information that can help reduce exposure to and the emissions of pollutants, leading to improved environmental and public health.

Air Quality Sensors Systems as Tools to Support Guidance in Athletics Stadia for Elite and Recreational Athletes.

While athletes have high exposures to air pollutants due to their increased breathing rates, sport governing bodies have little guidance to support events scheduling or protect stadium users. A key limitation for this is the lack of hyper-local, high time-resolved air quality data representative of exposures in stadia. This work aimed to evaluate whether air quality sensors can describe ambient air quality in Athletics stadia. Sensing nodes were deployed in 6 stadia in major cities around the globe, monitoring NO2, O3, NO, PM10, PM2.5, PM1, CO, ambient temperature, and relative humidity. Results demonstrated that the interpretation of hourly pollutant patterns, in combination with self-organising maps (SOMs), enabled the interpretation of probable emission sources (e.g., vehicular traffic) and of atmospheric processes (e.g., local vs. regional O formation). The ratios between PM size fractions provided insights into potential emission sources (e.g., local dust re-suspension) which may help design mitigation strategies. The high resolution of the data facilitated identifying optimal periods of the day and year for scheduling athletic trainings and/or competitions. Provided that the necessary data quality checks are applied, sensors can support stadium operators in providing athlete communities with recommendations to minimise exposure and provide guidance for event scheduling.

Internet of Things- (IoT-) Based Real-Time Vital Physiological Parameter Monitoring System for Remote Asthma Patients

In this paper, the design and implementation of an asthma patient monitoring system are presented. It can be categorized as an IoT-based device which can monitor heart rate, room temperature, humidity, air quality, nostril temperature, and oxygen saturation (SpO2) using an ESP8266 microcontroller and different sensors. All data will be sent to Firebase via Wi-Fi and then to patients and doctors via mobile applications and websites, respectively. Doctors can access this data via a website and instruct patients based on their specific needs. An ESP8266 microcontroller, a DHT11 temperature and humidity sensor, a MAX30100 pulse and SpO2 sensor, a MQ-135 air quality sensor, and an LM-35 temperature sensor have been used for the proposed system in this research. This system uses an efficient way to store all the data and information about the patient. It also carries their various test reports. The data is preserved in a proper way, which will help the patient or doctor retrieve any information in a smooth and swift manner. The software part of the system is divided into two parts. One of them is a mobile application that was designed by the Android Studio. The second one is the website, and it is also divided into two parts. HTML (hypertext markup language), CSS (cascading style sheets), and JavaScripts are designed for the front end of the website, and PHP (hypertext preprocessor) is used for the backend of the website. The device is tested on seven different real human test subjects, and the accuracy of that device is state of the art. It can measure the heart rate of a person along with their oxygen saturation with accuracy. Also, this device calculates the humidity properly as it changes the moment wet towels are put near the sensors and far from the sensors. It also shows the proper air quality as it has been tested with smoke to measure the extreme difference in the air quality. This system also has video calling and prescription features. Using this system, patients can consult with the doctor and get a prescription. Using this system, the vital physiological parameters of the remote patients can be measured and monitored. Any hospital or diagnostic center can be more efficient and organized by using a system. And patients are also freed from the hustle as they do not need to go and visit the doctor over and over again. The system is designed in such a way that it can look after the particular needs of a typical hospital or a diagnostic center and is able to provide easy and efficient storage of information related to patients.

A dual-path dynamic directed graph convolutional network for air quality prediction

Accurate air quality prediction can help cope with air pollution and improve the life quality. With the development of the deployments of low-cost air quality sensors, increasing data related to air quality has provided chances to find out more accurate prediction methods. Air quality is affected by many external factors such as the position, wind, meteorological information, and so on. Meanwhile, these factors are spatio-temporal dynamic and there are many dynamic contextual relationships between them. Many methods for air quality prediction do not consider these complex spatio–temporal correlations and dynamic contextual relationships. In this paper, we propose a dual-path dynamic directed graph convolutional network (DP-DDGCN) for air quality prediction. We first create a dual-path transposed dynamic directed graph according to static distance relationships of stations and the dynamic relationships generated by wind speed and directions. Then based on the dual-path dynamic directed graph, we can capture the dynamic spatial dependencies more comprehensively. After that we apply gated recurrent units (GRUs) and add the future meteorological features, to extract the complex temporal dependencies of historical air quality data. Using dual-path dynamic directed graph blocks and the GRUs, we finally construct a dynamic spatio-temporal gated recurrent block to capture the dynamic spatio-temporal contextual correlations. Based on real-world datasets, which record a large amount of PM2.5 concentration data, we compare the proposed model with the benchmark models. The experimental results show that our proposed model has the best performance in predicting the PM2.5 concentrations.