IoT System for Air Pollutants Assessment in Underground Infrastructures

George Suciu Mihaela Balanescu Carmen Nadrag R&D R&D R&D BEIA Consult International BEIA Consult International BEIA Consult International , Bucharest, Romania Bucharest, Romania [email protected] [email protected] [email protected]

Andrei Birdici Cristina Mihaela Balaceanu Marius Alexandru Dobrea R&D R&D R&D BEIA Consult International BEIA Consult International BEIA Consult International Bucharest, Romania Bucharest, Romania Bucharest, Romania [email protected] [email protected] [email protected]

Adrian Pasat Radu-Ioan Ciobanu R&D Faculty of Automatic Control and Computers BEIA Consult International University Politehnica of Bucharest Bucharest, Romania Bucharest, Romania [email protected] [email protected]

ABSTRACT friendly perspective. Also, it diminishes the negative effects on the health of the population, by reducing the emissions generated by This paper describes an IoT system capable of capturing car traffic. In addition, by limiting congestion and providing vital information about hazardous working environments and analyzes transport links in a city, the usage of subways also improves the the health risks associated with increased air pollution. The case overall quality of urban communities. Although all these benefices, study regards the underground transportation systems, which are the increased air pollutants concentrations from underground key components in commuting networks of large cities, providing environment ([2], [3], [4], [5]) had a negative effect on human fast and affordable transport for urban communities. First, a risk health, [6]. The main pollutant is represented by particulate matter analysis of the categories of people working in this space or (PM) which may be associated with an increased risk of commuting through the city using the subway was performed. carcinogenicity and non-cancer health effects considering their Furthermore, the situation in other similar environments and the metal rich composition, [6]. However, in order to maximize the main sources of pollution was analyzed. By designing and positive impact on the urban environment, underground transport implementing a WSN system could be managed to gather air systems must ensure a safe and healthy environment for both quality data and process the sensors measurements. The passengers and workers in this system. experimental results consist of a predictive model of PMs emissions which can aid in mitigating air pollution. The main objective of the paper is describing the system capable of capturing information about these work environments, the health of workers and even the network infrastructure deployed in these KEYWORDS environments, in order to analyses and present this information, and thus having more information about the environments of railway Air Quality, IoT, railway system, PMs emissions metro system and the workers, as well as helping in the Data Driven Decision Making, ensuring and thus improving the safety within 1 Introduction these hazardous operating environments (not just physical security The underground transportation systems are key components in but also the network security). commuting networks of large cities, providing fast and affordable transport for urban communities. For example, Despite the scientific and technical efforts to connect to everything network ensure (in 2017) the daily transit of over 600000 people that is happening in the field of mobile communications research, representing around 25% of total city inhabitants, [1]. there is still a need for agile and reliable solutions in hazardous industrial environments. Within this paper, a solution will be The benefits of metro transportation are numerous and are mainly developed in order to design a wireless communication system in driven by the reduction of car use, which leads to an environment- underground environments (railway metro system). The goal is to reduce air pollution exposure by using various types of Internet-of- In a different study the authors describe the air quality in the main Things (IoT) sensors that can capture data from this environment. metro station in Rome from the point of view of PM concentration. Various indoor and outdoor environments have been studied and 2 Related Work compared. Also, the paper highlights the influence of the air The metro system infrastructures and operation condition varied conditioning over the air quality of an indoor environment and significantly worldwide and the focus of air research work. A first compares the results obtained with the gravimetric procedure and study was performed in Newark, U.S and was focused on chemical the optical particle counter methods [10]. composition of PM and source emissions identification (traffic, A study conducted in 4 different metro stations in Athens provided power plants, incineration, braking operations and track-wheel a comparison of the air quality depending on the type of ventilation abrasion), [7]. Since then more studies were performed in all the implemented in the locations (natural ventilation or using air- continents. In general, the air research studies start with conditioning). The measurements were made continuously using identification of chemical species and measurements of air portable devices and the results also depended on how deep and pollutants concentrations. The primary air pollutants identified in how crowded the subway station is [11]. the in the metro air were PM, aromatic hydrocarbons, carbonyls and airborne bacteria, [8]. Must be noted that, in general the PM 3 Methods concentration in metro are higher than the those in outside The main pollutants identified in the literature inside the environments, [8]. For example, in Stockholm the PM underground areas are represented by PMx (particulate matter with concentration were 5 to 10 times higher than those measured at the diameter less than x µm) and gaseous pollutants (Volatile Organic most crowded streets [9]. Also, the research studies investigate the Compounds – VOC, nitrogen dioxide – NO2, carbon monoxide – influence of ventilation on metro air quality and develop measures CO, carbon dioxide - CO2, etc), [8]. Their concentrations vary to reduce air pollutant concentrations, [8]. depending on the degree of ventilation of the interior spaces as well In Beijing’s metro stations variations of PM concentrations as the temperature and humidity levels. appeared periodically with the trains arriving in the station, which The establishment of the most representative location to install the was caused by the piston wind effect produced by the train. measurement equipment in the Bucharest underground Measurements were also made inside the train that showed a transportation network is based on a risk analysis of the categories considerable fluctuation of PM2.5 from 220 µg/m³ to 370 µg/m³ of people working in this space or commuting through the city when the door opened. A total of 8 different rooms were measured using the subway (Table 1). The analysis includes identifying the in the working areas of the subway of which only 3 of them were exposure area, the risk factors associated with both the environment equipped with a ventilation system while another was closed. The and the type of activity being carried out, and the exposure time. measurement took place in a lightly polluted day and concluded The employees number working at the location of the with a higher concentration of PM pollution than the one outside, communications center varies between 3-5 people. Also, the the highest contaminated rooms were the ones with the poor categories of persons considered for the analysis are represented by ventilation while the closed room had the lowest PM concentration. passengers, system maintenance workers and qualified personnel PM2.5 to PM average ratio was 79.6% outside the subway while in 10 in the communication or command centers. the underground locations it was at 68.7% [2]. TABLE 1. HEALTH AND ENVIRONMENTAL RISK ANALYSIS Studies have shown that the concentrations of PM measured in railway metro systems are considerably higher than the values Risk Exposed individuals measured at the street level, mainly due to mechanical processes of Factors Passengers Workers Qualified employees the trains. Therefore, experiments were conducted in order to Exposure Staying in and Stations, Communication analyze the air quality improvement in the case of using high- area going through tunnels Centers, Command quality materials and solutions for the metro system [3]. metro stations Centers Type of Light physical Intense Static positions In addition to this, researchers have investigated the evolution of physical activity or physical generating PM10 pollution levels in a new section of a metro line in activity static position activity for pain/musculoskeletal compared to the values measured in a subway line section which short periods disorders. had been used for a longer period. The results for the two areas were of time Light physical activity for short comparable from a statistical point of view, as the pollutants were periods of time. transferred from the old section to the new one due to the piston Duration Waiting time in Maximum 8 8 hours per day effect [4]. of subway hours per day, activity/ stations and depending on Another study identified street traffic as being the pollution source exposure transit between the repair and with particles of dimensions below 500 nm in the subway station in them. maintenance process. which the study was conducted. The correlation was done based on Risk Atmospheric Atmospheric Atmospheric the comparison of the size distributions of PMs [5]. factors pollutants pollutants pollutants (PM10, (PM10, PM2.5, (PM10, PM2.5, PM2.5, VOC, NO2,

CO2), CO2, CO), CO2, CO), humidity,  one smart Fitbit Charge bracelet. humidity, humidity, temperature, stress temperature temperature related to work The main advantages of the LANCOM 1780EW 4G + router is: responsibility  cloud router device management through Lancom Cloud Management application; Analyzing the table above, the category of qualified employees of  providing reliable network security performance thanks to the Communication Centers or Command Centers are identified as the LCOS10 operating system; the category most exposed to environmental and health risks.  availability of a 4G LTE wireless connection. For this use-case a specific architecture was developed. At the In order to achieve the experimental model for wireless lowest level of the architecture the sensors and the actuators stand communications in hazardous environments and to identify the out, which will be responsible for measuring and collecting signal strength diminution of the router due to interference with information of the environment itself. These sensors will send the other access points, measurements were made at the location of the collected metrics to a main Gateway using wireless communication pilot project and in a controlled test environment. The experimental system. The Gateway oversees the coordination of all the sensors wireless communications model in hazardous environments is and will send the information to the Data Acquisition level. Data functional and has enabled data acquisition from sensor devices. processing/ data analytics level are responsible for pre-processing The data acquisition process is presented below. and processing the data regarding the environmental pollutants, monitoring the network. The last level includes data presentation For accessing data measured by Libelium devices, the user has to components and represent the interface with the system users. At login in the Meshlium platform. Data can be viewed and accessed this point, the data and metrics collected go through different stages directly or through the PhpMyAdmin administration tool. The data of enrichment, indexing, storage and analysis to offer this acquisition process monitored by the Libelium sensors was carried information to the subsequent stages of visualization and action. out as follows: Thus, the information analyzed will be offered to the presentation  accessing the Meshlium GW (Gateway) interface (Manager and application layers, to be presented to the final user through a System). friendly and intuitive interface. In the same way, within this phase,  accessing the Tools menu in order to connect to the MySQL it will be possible to define triggers that generate alarms or infer database interface where sensor data is stored and logging into the other events based on the metrics obtained. The architecture PhpMyAdmin interface; implemented is presented in Figure 1.  querying the database using query functions;  downloading the data selected by the query function. The data acquisition process monitored by the uRADMonitor device (D#1) was as follows:  accessing the uradmonitor.com WEB platform.  accessing the Dashboard (Device View) menu;  accessing the DATA tab;  select the period and parameters of interest;  downloading the selected database. The device D#1 was placed on the main hall far from the air admission points and will be used to characterize the average air quality inside the Centre. D#2 and D#3 devices was placed in two working rooms of the Communication Centre near outdoor air admission points. D#1 send data with a frequency of 1 minutes, while D#2 and D#3 send date at every 15 minutes.

The selection of the parameters (PM and PM2.5 hourly Figure 1: Platform architecture 10 concentration values) for which a simulation model was developed For the acquisition of environmental data and physiological was based on the preliminary statistical analysis of the measured parameters, the following equipment were tested, [17] and used: data and by comparison with the limit values provided in the E.U. environmental legislation [15].  one router LANCOM 1780EW 4G + [12];  one air quality monitoring station uRADMonitor Industrial, First step was represented by a qualitative analysis of the data based uRAD (D#1) [13]; on the comparison with standard data requirements (i.e.  two devices (D#2 and D#3) Libelium Plug and Sense SCP concentration values must be a positive or null value) and (Smart Cities Pro) air quality monitoring stations [14]; calculation of hourly average concentrations. The second step was  one Libelium Gas Board data acquisition board with air represented by a preliminary analysis using statistical descriptive quality monitoring sensors; methods (i.e. mean value, variation, standard deviation). The

dataset included in the modelling process are represented by PM10 and PM2.5 hourly average concentration values for a period of 11 days (in October 2018). In section IV are presented the results of this analysis and the predictive model developed. The predictive model obtained using a multiple linear regression method were tested for validation and the results are presented in the section V.

4 Results The comparison with the PM10 and PM2.5 concentration limit values provided by environmental legislation [15] indicates daily values exceedances for both pollutants. Thus, the value of the daily PM10 Figure 2: The daily variation of hourly PM10 concentrations concentration is exceeded in 66% of cases while the daily PM2.5 recorded by D#2 equipment concentration value is exceeded in 91% of cases.

For the selected parameters (PM10 and PM2.5), simulation models were developed and tested for values obtained in common spaces (common hall) based on concentration values measured at outdoor air admission points. The main statistical parameters for the data recorded by three of the monitoring equipment are presented in Table 2.

TABLE 2. STATISTICAL PARAMETERS VALUES FOR THE PM10 AND PM2.5 MEASURED CONCENTRATION

Statistical PM2.5 PM10 Parameter

D#1 D#2 D#3 D#1 D#2 D#3 Figure 3: The daily variation of hourly PM10 concentrations recorded by D#3 equipment Mean 47.75 42.52 79.02 64.66 43.14 81.27 The stepwise regression method was applied in order to achieve a Median 45.09 39.55 73.48 61.12 40.08 75.17 mathematical model for estimating concentrations of PM10 in the Standard 24.81 15.53 24.10 33.01 16.07 25.37 central corridor of the case study location. Since D#2 and D#3 are Deviation located near outdoor air admission points, they were considered as inputs, having as dependent variable the concentration values Variance 615.95 241.27 580.80 1089.55 258.54 643.68 measured in the central hall of the location with the D#1 equipment. The model equation is presented below. Minimum 9.89 16.33 44.65 14.30 16.61 45.42 Maximum 138.96 97.01 184.80 185.97 108.85 192.91 y = 1.270 × x1 + 0.347 × x2 – 18.610

Count 305 304 269 305 304 269 where: y - PM10 concentration in central corridor, µg/N푚 ;

Confidence ± 2.79 ± 1.75 ± 2.89 ± 3.71 ± 1.81 ± 3.04 x1 - PM10 concentration measured next to the admission level (95%) point in room 1 (D#2), µg/N푚; x2 - PM10 concentration measured next to the admission point in room 2 (D#3), µg/N푚. The analysis of daily PM10 hourly concentrations was necessary in order to identify possible patterns of concentration variations. Figure 2 and Figure 3 show the measurements variation for the data The high value of the multiple correlation coefficient (0.867) and acquired during the experimental test period. the F test (379.103), cumulated with the standard distribution of standardized residual values (Figure 4) and the linearity of the cumulative probability curve (Figure 5) indicates that the

mathematical model of estimating the PM10 in the central hall of the case study location are representative.

Figure 7: The daily variation of hourly PM2.5 concentrations recorded by D#3 equipment

In the similar way with the model for PM10 concentration, the Figure 4: Distribution of residual values for the estimation stepwise regression method was applied in order to achieve a model for PM10 concentrations mathematical model for estimating concentrations of PM2.5 in the central corridor of the case study location. The model equation is presented below.

y = 0.975 × x1 + 0.305 × x2 – 18.013

where: y - PM2.5 concentration in central corridor, µg/N푚 ; x1 - PM2.5 concentration measured next to the admission point in room 1 (D#2), µg/N푚; x2 - PM2.5 concentration measured next to the admission point in room 2 (D#3), µg/N푚.

The high value of the multiple correlation coefficient (0.884) and the F test (444.773), cumulated with the standard distribution of standardized residual values (Figure 8) and the linearity of the cumulative probability curve (Figure 9) indicates that the Figure 5: Cumulative probability curve for residual values for mathematical model of estimating the PM10 in the central hall of the the PM10 estimation model case study location are representative.

The analysis of daily PM2.5 hourly concentrations was necessary in order to identify possible patterns of concentration variations. Figure 6 and Figure 7 show the measurements variation for the data acquired during the experimental test period.

Figure 8: Distribution of residual values for the estimation model for PM2.5 concentrations

Figure 6: The daily variation of hourly PM2.5 concentrations recorded by D#2 equipment

The linearity of the representations of the measured and estimated

PM10 and PM2.5 hourly concentrations indicates that equations used predict very well the concentrations.

6 Conclusions

The pattern of the PM10 and PM2.5 hourly concentration values for both measurement points (D#2, D#3) show a high similarity. These

are explained by the higher percentage of PM2.5 from PM10 (98.64% for D#2 and 97.38% for D#3).

Considering also that PM10 and PM2.5 concentrations increase in the morning (corresponding to the rush hours) there is a strong indication that the main emissions source for these pollutants in the Bucharest Communication Centre of the railway metro network are urban road traffic.

The model for estimation of the PM10 and PM2.5 hourly Figure 9: Cumulative probability curve for residual values for concentrations on the central hall point of the case study based on the PM2.5 estimation model concentrations measured near to the outside air admissions point predict very well the concentrations. Nevertheless, the model needs 5 Testing and Validation to be developed and test on much larger set of data and will be

The models developed for PM10 and PM2.5 hourly concentrations realized in the next phase. Also, will be assessed several solutions were tested on a dataset for 16 hours. The comparison between the meant to reduce air pollution. By installing several air purifiers measured and estimated concentrations are presented in Figure 10 which also integrate various air quality sensors (PM2.5, VOCs, air and Figure 11. temperature, and relative humidity), we expect to achieve an important milestone in delivering a safer working environment.

ACKNOWLEDGMENTS

The work presented in this paper has been funded by UEFISCDI Romania through WINS@HI project (PN-III-P3-3.5-EUK-2017- 02-0038), Tel-MONAER project (subsidiary contract no. 1223/22.01.2018, from NETIO Project ID: P 40270, MySMIS CODE: 105976) and ESTABLISH project (PN-III-P3-3.5-EUK- 2016-0011).

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