Towards Indoor Radon Analytics: An OLAP-based

Multidimensional Approach

Rolando Azevedo

1,2 a

, Joaquim P. Silva

, Nuno Lopes

, António Curado

Leonel J. R. Nunes

and Sérgio Ivan Lopes

2,4 f

2Ai – School of Technology, IPCA, 4750-810 Barcelos, Portugal

CiTin – Centro de Interface Tecnológico Industrial, 4970-786 Arcos de Valdevez, Portugal

proMetheus, Instituto Politécnico de Viana do Castelo, Rua Escola Industrial e Comercial Nun’ Álvares,

4900-347 Viana do Castelo, Portugal

ADiT-Lab – Instituto Politécnico de Viana do Castelo, 4900-347 Viana do Castelo, Portugal

Keywords: Data Warehouse, ETL, Indoor Radon, IoT, OLAP, Data Analytics.

Abstract: Indoor radon represents a known hazard to public health, namely, its relationship with lung cancer. The

adoption of data analytics tools for indoor radon human exposure risk assessment is crucial for building

management decision-making and is a fundamental requirement for the implementation of remediation

measures. This work presents the implementation of a data warehouse and an OLAP cube as components of

a more comprehensive IoT-based system, which has been developed for continuous indoor radon gas

management in public buildings. The proposed data warehouse consists of a three-tier data storage structure

to store historical measurements. Although the adopted approach has been tested with a small number of IoT

sensors, the operation of the data warehouse and OLAP server assures that the system is viable and highly

scalable. The increase in the number of active IoT sensors deployed in new buildings, cities, and districts will

increase the richness of the data, which will help to foster even better models.

1 INTRODUCTION

Radon is a naturally occurring and chemically inert

radioactive gas that is produced from the natural

decay of uranium (

238

U) which can be found in rocks

and soil. Radon has no color, smell, or taste. It

accumulates in enclosed spaces as it easily escapes

from the ground into the indoor air. When the most

stable isotope of radon (

222

Rn) decays, it emits alpha

particles, beta particles, and gamma rays (Darby et

al., 2005). Due to its radioactive nature, it represents

the second cause of lung cancer after smoking

worldwide (WHO, 2017). Radon enters the body

mainly through inhalation and it is in the lungs that its

decay can cause damage in lung tissues. Radon and

its decay products have been classified as

https://orcid.org/0000-0001-7860-0039

https://orcid.org/0000-0002-0138-2456

https://orcid.org/0000-0001-8897-5061

https://orcid.org/0000-0002-5828-6086

https://orcid.org/0000-0001-5404-8163

https://orcid.org/0000-0001-6944-7757

carcinogenic since 1988 by the International Agency

for Research on Cancer (IARC) (Gaskin et al., 2018).

A study has shown that the risk of contracting lung

cancer increases by 16% for every increase of 100

Bq.m

-3

in radon concentration (Darby et al., 2005).

Worldwide, inhalation of radon contributes to more

than 40% of the annual dose of all ionizing radiation

(APA, 2010). Since Radon is a hazardous air

pollutant, when high concentrations are reached

inside buildings, European Commission issued in

1990 the recommendation 90/142/Euratom to

propose concentration limit values of 400 Bq.m

-3

for

old dwellings and 200 Bq.m

-3

for new dwellings (The

Commission of the European Communities, 2001).

The Directive 2013/59/EURATOM, issued in 2013,

forcing all member states to prepare a plan to limit

Azevedo, R., Silva, J., Lopes, N., Curado, A., Nunes, L. and Lopes, S.

Towards Indoor Radon Analytics: An OLAP-based Multidimensional Approach.

DOI: 10.5220/0011272800003269

In Proceedings of the 11th International Conference on Data Science, Technology and Applications (DATA 2022), pages 361-369

ISBN: 978-989-758-583-8; ISSN: 2184-285X

361

exposition to radon gas and sets a concentration limit

of 300 Bq.m

-3

(European Commission, 2014).

Portugal transposed this directive into national law

effectively since April 3, 2019, through Decree-Law

No. 108/2018 (Curado et al., 2019).

Given the existing risk and the legislation in force,

it is important to develop methodologies to evaluate

and quantify the effective accumulated dose for the

occupants of a building during a given period to

implement remediation measures if necessary. Radon

assessment campaigns can also be used to understand

which factors, (internal and external), may impact

indoor radon concentration. This has been the case in

several studies conducted in the northern region of

Portugal, which is a high-risk area (Curado et al.,

2017). In the study, the authors used handheld meters

to analyze the indoor radon concentration in three

houses during two distinct year seasons. The results

showed that human occupation along with passive

ventilation strategies directly affected radon

concentration. Similar results were also found in

another study conducted in nine public buildings in

the Alto Minho region (Curado & Lopes, 2016).

However, while measurement campaigns are useful

to assess the problem, they cannot implement real-

time mitigation measures. To implement real-time

mitigation is necessary to have systems that are

continuously measuring radon concentration inside

buildings. Since society is also increasingly

concerned with energy-saving and energy efficiency,

these systems can integrate other indoor air quality

parameters and information on building occupancy to

dynamically adapt remediation measures that will

keep the balance between radon concentration and

thermal comfort. When a building is occupied, radon

levels should be at an acceptable level keeping

atmospheric conditions within a comfortable range,

but outside occupancy intervals energy savings can be

maximized without adversely affecting radon levels

in periods of occupancy.

Thus, the RnMonitor project (Online Monitoring

Infrastructure and Active Mitigation Strategies for

Indoor Radon Gas in Public Buildings in Nothern

Region of Portugal) developed a system capable of

online monitoring and actively mitigate radon

concentration (Martins et al., 2020). The

methodology described by Martins et al. corresponds

to one of the RnMonitor platform modules that

aggregates and displays the data collected in a set of

critical buildings selected after an assessment

campaign, during the first stage of the RnMonitor

project execution. As there was no commercially

available sensor to support the project requirements,

an IoT-based multi-parameter sensor was developed

for online monitoring of radon gas and other indoor

air quality parameters. The measurements taken

inside each compartment are transmitted hourly via

radio communications to a local server. The proposed

architecture uses a time-series InfluxDB database that

records short-term measurements. Furthermore, it

was implemented a data warehouse capable of storing

long-term measurements and providing advanced

analysis capabilities was yet to be implemented.

This paper presents the development and

implementation of a multidimensional data

warehouse that enables the RnMonitor platform not

only to store long-term measurements but also to

offer the possibility of using OLAP cubes to explore

the data in a multidimensional way. Moreover, this

work also presents the modelling and implementation

of the ETL process for the creation of a data

warehouse. It was coupled an OLAP server that will

make use of the data warehouse. This document is

structured as follows: Section 2 presents continuous

monitoring systems for radon or air quality; section 3

presents the methodology to develop de data

warehouse; results are presented and discussed in

section 4; and, in section 5, conclusions are

summarized.

2 RELATED WORKS

Over the years, several techniques have been

developed for the measurement of radon

concentration in air. Some of the best known are

activated charcoal detectors, alpha-track detectors,

and continuous radon detectors. Many campaigns are

done with activated carbon detectors because they are

easy to use and do not require electrical power during

a collection campaign that lasts from two days to

about a week. During the sampling period, the radon

gas is absorbed by the activated charcoal following

Van Der Wall’s basic principle. The radon

concentration is later determined in the laboratory by

counting the gamma-ray emissions of lead (

214

Pb) and

bismuth (

214

Bi), which are decay products of radon.

Andreas C. George (1984) describes the use of this

type of detector for the measurement of radon

concentration.

Martín Sánchez et al. (2012) used an activated

charcoal canister to identify 130 workplaces to

perform a long-term study in Extremadura (Spain).

The authors used this type of device since the

exposure time required was only two days. Although

these detectors are affordable and easy to install, they

can only determine the average concentration. When

it is necessary to measure the radon evolution over

DATA 2022 - 11th International Conference on Data Science, Technology and Applications

362

time, the use of portable electronic sensors is an

advantage. In addition to being able to collect data

over longer periods, some devices allow one to

download the measurements for analysis. Using 13

portable radon monitor Airthings Corentium Plus in

13 rooms of a school in Viana do Castelo, Azevedo et

al. (2020) & (2021), analyzed the evolution of radon

concentration in the rooms over 41 days.

These equipment do not allow for active

mitigation as the measurements are not processed in

real-time by the device or sent to a cloud server for

viewing, alerting, or activating a mitigation system.

Zheng et al. (2016) developed a system for air quality

monitoring using IoT techniques and Low Power

Wide-Area (LPWA) wireless technology to transmit

the data to the cloud where it is processed. Although

this air quality monitoring system does not include

radon measurement, the data transmission technology

is interesting because it can cover a wide area. It was

with this aim that a system that combines the use of IoT

technologies and Low Power Wide-Area (LPWA)

network communications has been developed (Sérgio

I. Lopes et al., 2019). This continuous monitoring

system, which the authors have called RnMonitor,

makes use of IoT technologies and uses a license-free

sub-gigahertz bidirectional LoRa communication to

send the measurements. In a test using three

LoRaWAN Gateways, the authors successfully

covered the center of Viana do Castelo city with signal

always below -100 dB while LoRa has an input

sensibility of -148 dB (Sergio I. Lopes et al., 2019).

The reader should notice that the development of the

data warehouse presented in this paper is part of the

RnMonitor platform. On the client-side, RnMonitor

offers a front-end application that allows you to view

the measurement sites with cartography-based

navigation and a dashboard that makes use of Grafana

to visualize the measurements over the last 24 hours, 1

week or 3 months. Additionally, Pereira et al. (2020)

developed the RnProbe which is an IoT Edge device

capable of measuring radon concentration,

temperature, relative humidity, atmospheric pressure,

and CO

In the literature review, we did not find any online

radon monitoring work combining the use of a data

warehouse and OLAP. García-Tobar (2020) used an

assessment campaign of two dwellings of a

residential building in Madrid to build two OLAP

cubes from the data. In other research domains, it is

possible to find online monitoring systems that

implement data warehouses. Soares et al. (2018)

developed a data warehouse to store the water

consumption of the municipality of Esposende in

Nothern Portugal and thus monitor and analyze the

water consumption to reduce water losses and

improve water consumption management. Tshering

et al. (2021) has created an IoT-based platform, using

Apache Hadoop and Apache Kylin analytics engine,

for continuous air quality monitoring to measure air

pollution using a PM

2.5

particulate sensor.

3 SYSTEM IMPLEMENTATION

The proposed system allows the record of

measurements in a multidimensional data warehouse

and the use of OLAP cubes to explore the data using

MDX queries. The data warehouse thus created

allows keeping the historical data and pre-calculated

measurements beyond the 2 years limit of the

InfluxDB time series database.

3.1 RnMonitor Data Source

The data warehouse has two data sources provided by

the RnMonitor platform: the application database

(AppDB) and the time series database (TSDB). The

data contained in these two databases can be accessed

through a RESTful API providing several endpoints.

The endpoints are protected using JSON Web Tokens

(JWT) that must be sent in the header of each request

made by the user.

The AppDB database is an open-source

document-oriented NoSQL database MongoDB.

Unlike relational databases, that store information in

columns and rows, this type of database stores

separate documents within a collection. The TSDB

database is also an open-source database widely used

in real-time monitoring applications, designed to be

able to handle a high volume of queries and writes per

second. Figure 1

shows the data model of the two

databases of the RnMonitor platform.

The raw measurement data generated by the

sensors are stored in the TSDB database in the

“

Measurements” table. There are ten attributes

"fieldn" which correspond to the various air quality

parameters measured where "n" corresponds to the

"field_id" of the table “Field”, a table that contains

information about each of the parameters. Currently,

the parameters analyzed are radon, temperature, CO

atmospheric pressure, and relative humidity.

In the AppDB database, one of the main tables is

the table "Polygon", which can have four different

types: compartment, building, county, and district.

This table contains a parent-child relationship

through an attribute indicating the parent polygon.

Note that a compartment has always a building as a

parent, a building has always a county as a parent and

Towards Indoor Radon Analytics: An OLAP-based Multidimensional Approach

363

Figure 1: RnMonitor Databases model.

a county has always a district as a parent. The only

polygon that has no parent is the so-called district

because it is the highest order polygon. The

“MeasurementSet” table is used to record the

location of each of the sensors, as the same sensor

may be used to take measurements in one room inside

a building and later be removed to take measurements

in a room inside another building in a different

county.

The sensor has no GPS locator, so its current

location is only possible by looking at the

"MeasurementSet" table.

The table "OccupationProfile" allows the

creation of different occupation profiles for the same

room or building for different users. This can be used

to calculate the accumulated radon exposure dose for

different workers depending on the time they spend

in the compartment. The "Notification" and “User”

tables, although implemented, do not contain useful

information for the implementation of the data

warehouse.

3.2 Data Warehouse

The data warehouse was implemented considering

the AppDB and TSBD data models and the data

analysis goals. The data warehouse model uses a star

schema for easier understanding and faster queries.

The model shown in Figure 2 is composed of three-

dimensional tables and a fact table and two support

tables. The "Dim_Polygon" dimension is the most

important dimensional table. This table contains a

parent-child relationship, where each polygon

references its parent polygon through a foreign key

that corresponds to the id of the parent polygon.

The fact table “fact_measurement” contains

three calculated measures: “radon_kpi_pt” which

corresponds to the value of radon/300 being 300

Bq.m

-3

the radon limit in the Portuguese legislation;

“radon_kpi_oms/100” being 100 Bq.m

-3

the limit

value advised by the WHO; “thi_value” which

corresponds to the Temperature-Humidity Index

(THI) value. This last attribute corresponds to the THI

index, which represents the combination of

temperature and humidity to measure the degree of

thermal comfort experienced by an individual

indoors. This index, developed originally by Thom

(1959), combines the wet and dry bulb temperatures

on a scale to mimic the thermal sensation of the

human being. The Nieuwolt's (1977) modified THI

correlates air temperature and relative humidity,

allowing a more straightforward approach to rapidly

assess indoor thermal discomfort based on the

measurement of hygrothermal parameters. The

Nieuwolts THI is defined by the following formula:

THI = 0.8×T+(T×RH)/500

where T corresponds to indoor air temperature and

RH to the indoor relative humidity.

DATA 2022 - 11th International Conference on Data Science, Technology and Applications

364

Figure 2: Data Warehouse Model.

The “Closure” table allows keeping the transitive

closures of the parent-child relationships of

“Dim_polygon”. The hierarchy between parent and

child is kept by the distance attribute that determines

the distance between parent and child tuples. This

table is necessary to the hierarchy definition in OLAP

cube schemas when implemented on Mondrian

OLAP Server. The table “thi_occupation” is used to

normalize the non-quantitative attributes by

removing them from the fact table and creating a table

to register the different combinations. The

combinations of the thi_occupation are defined in

Table 1.

Table 1: Thi_occupation table content.

id thi_description thi_lower thi_uppert Is_occupied

Too cold 0 8 false

Too cold 0 8 true

Need for heating 8 21 false

Need for heating 8 21 true

Comfortable 21 24 false

Comfortable 21 24 true

Need for ventilation 24 26 false

Need for ventilation 24 26 true

Too hot 26 99 false

Too hot 26 99 true

No THI data 0 0 false

No THI data 0 0 true

The range values for THI are defined by “thi_lower”

and “thi_upper.” For each “thi_description” that

corresponds to a different THI interval, we have two

possibilities for the compartment occupation

represented by the “is_occupied” column.

3.3 ETL Process

The ETL process allows the creation of the data

warehouse by extracting data from the two databases

of the RnMonitor platform, manipulating and

transforming the data, before loading it into the

respective dimensional and fact tables of the data

warehouse. The ETL process is executed once a day,

thus loading the measurements performed in the

previous 24 hours. The use of a data warehouse will

allow a better understanding of radon behavior and

discover patterns through advanced analysis

techniques. That is why a daily update of the

measurements is sufficient since mitigation actions

can be triggered by the RnMonitor platform based on

the online radon readings loaded in the TSDB

database.

The ETL process was developed using Pentaho

Data Integration (PDI) through the Spoon graphical

interface. The transformations download data from

both RnMonitor databases through several RESTful

API endpoints. The ETL process is triggered by a

single job that has the function of cascading several

transformations. The execution of a transformation

always depends on the conclusion of the previous

one. There are two different ETL process execution

flows. The first flow represented in the Figure 3

corresponds to the initial process. It is executed only

once and serves to create the data

Towards Indoor Radon Analytics: An OLAP-based Multidimensional Approach

365

Figure 3: Initial ETL execution flow.

Figure 4: Update ETL execution flow.

warehouse and the various tables that compose it. It is

during this phase that all the dimensional tables are

populated and all the measurements that can be obtained

from the TSDB are loaded. The second flow represented in

Figure 4 corresponds to the update process. This process

flow is the one executed daily. In case of any change in the

polygons data, the “dim_polygon” dimension is updated

using a Kimball slowly changing dimension of type 2. The

closure table is recreated whenever the “dim_polygon”

dimension has new polygons. During this execution flow

the fact table “fact_measurements” is loaded with the new

measurements since the last update even if the system was

down for several days.

3.4 OLAP

Online Analytical Processing (OLAP) is a technology

that is part of many Businesses Intelligence (BI)

applications and allows for complex analytical

calculations. Aggregations, merging, and grouping in

a relational database are not efficient. These

operations are faster using OLAP since the data can

be pre-calculated and pre-aggregated. Our solution

provides an OLAP server to explore the cubes using

MDX queries, and for that, we have used Mondrian

as our OLAP Server. The data cube granularity is

determined by combining the levels corresponding to

each cube axis. We can change the level of granularity

to a finer one or coarser one, producing a different

cube measure value.

We can map members of the lower hierarchy to

members of the higher hierarchy. With the members

existing in our dimensional tables, the hierarchies of

Figure 5 can be implemented on the cube.

Figure 5: Dimension Hierarchies.

For the Mondrian OLAP server to use the data

warehouse created, it must use a cube schema file.

This XML file contains the definition of one or more

OLAP cubes.

We can see the graphical representation of the

cube schema in Figure 6. In this definition, we find

the three dimensions with their respective hierarchies

and levels. The dimensions, Time, and Date are

defined outside the cube to be used in several cubes.

The cube makes use of these dimensions through

dimension usage. In “Dim_Polygon” dimension,

only one hierarchical level is defined. since the

DATA 2022 - 11th International Conference on Data Science, Technology and Applications

366

relation between the polygons is defined by using a

“closure” table, as the schema shows.

Figure 6: RnMonitor cube.

4 DISCUSSION

The data warehouse was validated by checking that

the contents of the data warehouse and the data

available in the two databases of the RnMonitor

platform, RawData and AppData. The data

warehouse content is structured according to the

multidimensional schema. It contains all the records

of the measurements gathered by the sensors since the

beginning of the sensors' measurements. By 12 March

2022, the database contains more than 121,000 radon

measurement records. The first measurement took

place on 15 May 2015, and after more than 33 months

the data warehouse has been updated daily proving

that it supports long-term data recording (over 24

months).

Table 2: Data Warehouse content.

Total number of measurements 121 735

Data collection starting date 2019/05/15

Districts 3

Counties 10

Buildings 18

Compartments 22

The data warehouse will be used to create tools

and develop strategies for radon mitigation. Table 2

show that the available measurements took place in

22 compartments of 18 different buildings located in

10 different counties which correspond to 3 different

districts.

Table 3 shows the number of records per sensor in

more detail. Currently, seven active sensors are

gathering hourly measurements for the RnMonitor

platform. More details about the implementation of

the active sensors can be found in Pereira et al.

(2020).

Table 3: Measurements by active sensor.

Sensor Measurements Start Date

D001 9554 2019-05-15T15:00:00

D003 22811 2019-05-15T15:00:00

D004 9545 2019-05-21T21:00:00

D007 8432 2019-07-05T00:00:00

D009 18246 2019-05-28T20:00:00

D0011 10273 2019-11-09T00:00:00

D0012 16457 2019-11-12T22:00:00

The validation of the OLAP server aimed to verify

that the cube schema was functional and to make sure

that MDX queries returned the expected results. As

the Mondrian instance provides a graphical interface

to test MDX queries, this functionality was tested

using queries that correspond to simple OLAP

operations. Since we installed the instance on a

remote server, we ran the test through the browser of

the Windows operating system computer and

accessed the URL serving the GUI web page.

Although the number of sensors that are carrying out

the measurements is small, the perspective is to

increase the number of sensors once the validation of

the operation of the data warehouse and OLAP server

confirms that the system is viable and has room to

grow. The increase in the number of active sensors

and the planned extension to other buildings, cities,

and even districts will greatly enhance the richness of

the data. A larger and more diversified data set will

allow producing better models.

5 CONCLUSIONS

The assessment of indoor radon concentration and the

mitigation of the associated exposure risks in public

buildings becomes mandatory because European

directives force member states to act to reduce the

Towards Indoor Radon Analytics: An OLAP-based Multidimensional Approach

367

indoor exposure risk. The exposure to high radon

concentrations increases the risk of developing lung

cancer. This risk increases in areas with a specific

geological constitution and poorly ventilated

buildings. These two factors are prevalent in public

buildings in the center and northern Portugal. In this

context, the RnMonitor platform was created to

perform continuous indoor radon monitoring in

several public buildings in the North of Portugal. This

paper presents the development of a data warehouse

capable of storing all the measurements’ history and

some derived measures, which has been integrated as

an additional module with the RnMonitor platform.

The data are loaded to the data warehouse through the

execution of an ETL process created for this purpose.

An OLAP server has been coupled to the data

warehouse to support OLAP cubes and business

intelligence tools.

ACKNOWLEDGEMENTS

This research is a result of the project TECH –

Technology, Environment, Creativity and Health,

Norte-01-0145-FEDER-000043, supported by Norte

Portugal Regional Operational Program (NORTE

2020), under the PORTUGAL 2020 Partnership

Agreement, through the European Regional

Development Fund (ERDF). R.A. was supported by

operation NORTE-06-3559-FSE-000226, funded by

Norte Portugal Regional Operational Program

(NORTE 2020), under the PORTUGAL 2020

Partnership Agreement, through the European Social

Fund (ESF). L.J.R.N. was supported by proMetheus,

Research Unit on Energy, Materials and Environment

for Sustainability – UIDP/05975/2020, funded by

national funds through FCT – Fundação para a

Ciência e Tecnologia. António Curado co-authored

this work within the scope of the project proMetheus

– Research Unit on Materials, Energy and

Environment for Sustainability, FCT Ref.

UID/05975/2020, financed by national funds through

the FCT/MCTES.

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