The paper presents the efforts to clarify the usage of environmental data as exposome data that affect human health. According to the medical scientists, the exposome includes all exposure environmental factors, such as chemical and nonchemical agents, socio-behavioral and psychological factors as stress, diet, endogenous and exogenous factors from the whole lifespan. We consider the opportunities of combining a cloud-based Personal Health Record (PHR) with a particular patient's disease and exposome data gained from environmental databases connected with date, time and location of measurement as an influential factor of their good behavior. The main prerequisite for this concept has to be the existence of reliable sensors that have to provide the needed data for this purpose as well as PHR data, secured by patients. If the patient with some chronic disease will have reliable and available exposome data in a machine-readable format, these data can be used for assessment of the health risk for their specific disease connected with this specific environmental pollutant. This type of patient-centric possible data integration has to bring many benefits for patients and medical staff in the process of improvement of patient's care and self-care.
Many efforts have been made to monitor the environmental parameters as factors that influence biological systems. According to the demands of EU regulative, the environmental parameters have to be measured and controlled as they affect human health and other biological systems, that are an obligation of government and municipalities authorities, especially now, in the era of IoT.
One of the biggest challenges in the next decade will be how the
combination of genome and exposome data (as a whole of human exposures from birth to
death) will contribute to reveal the risk factors for particular disease [
Nowadays, it is a widely known fact that these massive data can hold many capabilities if they are processed and prepared appropriately. In the healthcare industry, these data can be reliable sources for environmental risk of some disease assessment, the risk for surveillance and population health management. Therefore, considering the amount of data generated from environmental parameters measurement, they can optimize the potential of healthcare environmental risk assessment for some specific disease in specific locations. These data are the key for optimizing the risk decreasing for some diseases taking into consideration the possibility for changing some habits, place of living and time for some activities, from patients and medical practitioners’ perspective. It is a huge opportunity for data scientists to further improve environmental healthcare standards, given the overwhelming amount of environmental data being generated and stored. Their analysis can be very important in many management, policy-making and state levels.
In this IoT time, when the patients have an opportunity to have their own
Personal Health Record (PHR) in a cloud environment [
The paper describes how the real implementation of a cloud-based PHR
system in the real pandemic environment with Cross4all project [
The proposed model takes into account one of the environmental parameter measurement for risk assessment. After the introduction, related works for this concept are considered. The next section describes some prerequisites for the concept implementation. After this section, some insights for time-series databases for environmental exposure data are considered and data visualized, providing some ideas about their integration with PHR data. The subsequent section discussed some usage of this kind of exposome data in specific disease concerns and propose some future usage. The concluding section considers the main idea of the paper and possible integration with PHR data and concludes the paper by drawing the possible ideas for future work. 2
The increased e-health possibilities and development of health information
technologies nowadays creates a broad range of new opportunities in order to
improve the healthcare services for citizens [
The important prerequisite for implementing the concept of e-health and
increase the healthcare digital competency is the implementation of EHR,
electronic medical records (EMR) and PHR. It is viewed as a critical step towards
improvements in quality and efficiency in the healthcare system in many
European countries [
The interoperability in healthcare can be explained as the possibility of
exchanging healthcare data between two or more interconnected systems and can
be understood in different ways. We choose the definition of Metzger et al. [21],
where the interoperability of healthcare information system is defined as: ‘the
capability of heterogeneous systems to interchange data in a way that the data
from one system can be recognized, interpreted, used and processed by other
systems. This kind of interoperability can help in our intention to integrate our PHR
data with exposome data [
The concept of exposome encompasses all non-genetic exposures of an individual from its conception to its lifetime. All of these influences complement the genome. The use of holistic and data-driven approaches that are similar to those defining genomic structure expects the concept of Exposome to result in advances in our understanding of the complex ecological component of disease etiology. The Exposome data are designed to include three overlapping and complementary domains: 1.
A general external domain including macro-level factors such as climate, urban environment and societal factors; 2. An individual external domain including agents such as environmental pollutants, tobacco smoke, electromagnetic fields, diet and physical activity; and 3. A specific internal domain including gene expression, inflammation, and metabolism, often assessed through high-throughput molecular omics methodologies such as transcriptomic, proteomics and metabolomics [24].
Nowadays there are developed and are possible to apply novel tools and
methods to obtain robust estimates of chemical and physical exposures in the
outdoor environment (the outdoor exposome), focusing on key outdoor exposures
(like outdoor air pollutants, noise, green space, UV radiation) [25]. Some of the
measurement models that help the analysis are regression of air pollution in land
use, urban noise maps, land use maps, raster maps of land surface temperature,
building density, population density, connectivity, walkability and public bus
transport map information for the built environment, and meteorological data,
etc. Data from existing regulatory monitors were used to support the
extrapolation of ambient air exposure models. Such data are collected and public available
by the Ministry of Environment and Physical Planning [
The effect of the exposome on specific highly prevalent health outcomes
are pre- and post-natal growth and obesity, asthma and respiratory function, and
neurodevelopment. Besides inter- and intra – individual variability in specific
subpopulations or strata that are of importance to Exposome studies should be
characterized, including those in critical periods of life (including in utero,
early life, and old age) where susceptibility to adverse consequences of exposure
may be increased. Such subpopulations exist in the European Union FP7-funded
HELIX [
Humans are exposed to thousands of species with great intra-species diversity,
which demonstrates that the human Exposome is highly dynamic and influenced by
spatial/lifestyle and seasonal variables [26]. The concept of an Exposome network
based on the extensive interactions among the organisms and associations between
organisms and chemicals can be partitioned into a stable human-centric cloud and
a more dynamic environment-centric cloud. That way the data will be valuable for
many scientific fields, including public health, microbiome, environmental science,
evolution, and ecology. Human-centric cloud will store PHR and its architecture
is stable and highly secured [
An obstacle for dealing with environmental factors and the parameters
measured is the lack of sufficient knowledge of the relation with diseases. 24% of
the world’s deaths are linked to the environment (2016) [
Air pollution can increase the risk of respiratory diseases, increasing the
susceptibility to viral and bacterial infections. Some studies suggest that small
particles in the air facilitate the spread of viruses, as well as the new Covid-19,
in addition to direct person-to-person infection. However, the effects of exposure
to particles and other pollutants are poorly understood. The possible reasons for
the patient’s health depend a lot on the environment in which he is, resides and
lives. The latest finding is that among several environmental, health and
socioeconomic factors, air pollution and particulate matter (PM2.5), as its main
component, result as the most important predictors of patient health. It has also been
found that emissions from industry, farm and road traffic – in importance – may
be responsible for more than 70% of deaths nationwide [
The PHR data existence is one of the prerequisites for creating such a model
for usage of environmental data as Exposome together with PHR data [
Collecting and analyzing big data of air pollution with labeled thresholds can
give efficient metrics for the environmental factors and exposure of personal
health. For example, in Fig. 1 we can see real-time metrics for a specific location
and the dynamics of data collected in the time frame. Limits and target values
for SO2, NO2, CO, PM10, PM2.5, O3, benzene, PAHs and heavy metals are
defined in order to protect human health. The alert threshold indicates a level
of concentration above which there is a risk of short-term exposure to human
health as a whole and if immediate steps need to be taken to improve air quality.
The atmosphere inevitably plays a massive role in our health such as the impact
on DNA damage, metabolism, skin integrity, and lung health. Also, poor indoor
air quality can cause various infections, lung cancer, and chronic lung diseases
such as asthma. The following table defines the alert thresholds for SO2 and
NO2 concentrations, as well as the information and alert thresholds for ozone
and PM10, marked on the official website of the Ministry of Environment and
Physical Planning [
To improve air quality and minimize pollution-related deaths, we must identify, measure, and analyze our atmosome data to study how the air we breathe affects our health. Exposome research is expanding rapidly and public environmental data is becoming more detailed and accessible. The sensors collect data and send the readings to the cloud. The cloud system provides a REST API for retrieving and posting data. It also comes with an API to query based on specific locations. Users and researchers can easily collect and parse this data to generate graphs and study patterns over time. Fig. 2 shows a sample of collected data from different sensors on different locations labeled/tagged with code “shifra” (eq. 7948 – area “Jeni Maale”,7974 – area “Oblasta”, 7975 – area “Shirok Sokak” etc.).
Many tools provide an interactive graphical user interface to adjust the parameters of analytical methods (e.g., via sliders or checkboxes). Visualization views can usually be adjusted via common view navigation (zoom, pan, and rotation), dynamic queries, time frames etc. Such a tool is Grafana where the visualization of the data over the time frame is remarkable through different panels and efficient query languages like InfluxQL, Flux, PromQL or PostgreSQL (Fig. 2).
To improve air quality and minimize pollution-related deaths, we must identify, measure, and analyze our atmospheric exposome (atmosome) to study how the air we breathe affects our health. Exposome research is expanding rapidly and public environmental data is becoming more detailed and accessible. In North Macedonia, the problem with air pollution is alarming because as we can see from the data collected in Fig. 2 and Fig. 3 there are quite a high number of measurements that are above the critical threshold and that period represent the red zone especially for the people with chronic diseases. That is one of the main reasons for a higher rate of deaths (around 30%) compared with other countries with higher awareness for the exposome relations to public health.
Our system is very effective in detecting alert events, because of its
capabilities to collect and send data to the cloud in real-time. Particulate matter refers to
mixtures of microscopic solid and liquid particles suspended in the air. Two types
of particulate matter are most relevant to air pollution: PM10 and PM2.5. PM10
refers to particles that are between 2.5 and 10 microns; some examples of these
include dust, pollen, and particles of mold. PM2.5 consists of fine particles that
are 2.5 microns in diameter or less; fuel combustion, cigarette smoke, aerosols,
and more can form them. Particulate matter is a health risk because it is small
enough to be inhaled and deposits itself in the airways of the human body. The
smaller particles can even lodge themselves deep in the lungs or enter the
bloodstream. Even short-term exposure to PM10 has been associated with worsening
respiratory diseases and can lead to emergency room visits. Long-term exposure
(months to years) has been linked to premature death, especially in people with
chronic conditions, and leads to reduced lung function in children [
If a physician or user could access this kind of data regularly, they would gain insights on how their behavior and surroundings affect human bodies and take tangible steps towards staying healthy in traffic on vacation or other similar situations.
When it comes to integrating data, Chris Gennings cautioned that big data
are not always better. She emphasized that big data are usually more complex
and should be used to conduct hypothesis-driven and confirmatory research, not
just used in an exploratory manner. Another concern with big data is that they
can make any finding look significant in the traditional statistical sense, but they
may have an effect size that might not be critically meaningful. One strategy is to
integrate data from disparate study types, such as those linking environmental
exposures and health outcomes on the assumption that exposure in a certain locality
might be relevant to health outcomes in those localities. Gennings noted that a
variation of this strategy involves linking human data with experimental study
results, as in the case of the European Union’s EDC-MixRisk project, which
links laboratory studies on endocrine-disrupting chemicals with data from two
birth cohorts to suggest what realistic relevant exposures might be and generate
a risk assessment [
Geocoding using mobile sensors, zip codes, and questionnaires will be
important for using EHR data in environmental health studies given that most
environmental exposure data are not captured in the EHR today [
Pediatric Research using Integrated Sensor Monitoring Systems (PRISMS)
project from the National Institute of Biomedical Imaging and Bioengineering
launched in 2015, aims to develop sensor-based, integrated health monitoring
systems for measuring environmental, physiological, and behavioral factors in
pediatric epidemiological studies of asthma and eventually other chronic
diseases. Asthma affects 1 in 12 people. The idea is that patients and various
sensors will interact with PRISMS through smartphones or smartwatches that will
securely upload data to the project’s informatics platform and data coordinating
center. This individual-level data will be linked with external environmental data
from sources such as Environmental Agency’s monitoring networks or pollen
counts and with EHR data. After synchronizing and integrating these data
sources, PRISMS investigators will conduct predictive modeling that can be fed back
to the patient or parent to both engage the patients and encourage patient
compliance with an asthma management plan. Health care providers may also receive
information from the system. Children participating in PRISMS will receive
smartwatches that can collect real-time data from built-in GPS, accelerometers,
and gyroscopes, which will provide a measure of the child’s activity and
microenvironment. Children will also carry portable Bluetooth-enabled spirometers,
so-called smart inhalers that provide a geolocation and time stamp with every use
and sensors that can sample the environment and provide a personal
measurement of exposure to air pollution. The project team is working on methods to
process the torrent of data these sensors will generate, align the different sensor
data streams, integrate them with external data sources, and use advanced
analytics, including machine learning, to cluster patients and make predictions from
individual baseline measurements, explained Sandrah Eckel from the University
of Southern California [
Experimental exposure to PM results in oxidative stress, airway
hyper-responsiveness, and airway remodeling, either alone or in combination with
allergic sensitization. Short-term exposure to ambient PM2·5 and PM of diameter
2·5–10 μm in prospective cohorts of asthmatic children and adults has been
associated with asthma symptoms, especially in children with allergic
sensitization. Long-term exposure to PM is associated with poorly controlled asthma and
decrements in lung function in children and adults. Several studies in children
and adults have shown associations between short-term and long-term exposure
to PM2·5 or PM10 and increased healthcare use. These associations are generally
partially attenuated but persistent after adjustment for co-pollutants [
Some evidence suggests PM is a cause of incident asthma. Independent
associations between exposure to PM10 in utero and during infancy with asthma
diagnosed by a doctor were identified in a nested case-control study within a
large birth cohort. Although several studies have identified associations between
asthma prevalence and exposure to outdoor PM, this finding has not always been
consistent. Furthermore, PM is frequently strongly correlated with ozone,
nitrogen oxides, and Sulphur oxides, serving to confound these associations. In
summary, substantial evidence supports the idea that ambient levels of PM exacerbate
existing asthma, particularly by contributing to oxidative stress and allergic
inflammation, and some evidence exists in support of PM as a cause of new cases
of asthma (Fig. 4) [
The examples created from environmental data for this paper highlighted just a couple of possible usage of the environmental Exposome and their integration with PHR data, as is shown in the Fig. 5. This research has to explain the proposed model by using these environmental exposome data. The model should be validated through examples of risk assessment for specific patient’s PHR as an influencing factor of these pollutants to some specific patient’s disease in order to have a clear understanding of this influence of the patients’ health. For this purpose, methods for machine learning or decision-making by evaluating the associated costs and health benefits of mitigation actions against climate change can be used to create some algorithm for risk assessment that contains the mathematical estimation and modeling of several processes, including population estimates, population exposure to pollutants, and adverse health impacts assessment through specific concentration-response functions [27]. A portion of complex disease risk is likely due to the interaction of inherited genetic and non-inherited environmental factors. A substantial body of research on the effects of air pollution on asthma has been published in the past years, adding to the body of knowledge that has accumulated over several decades. Presently, short-term exposures to ozone, nitrogen dioxide, Sulphur dioxide, PM2·5 is thought to increase the risk of exacerbations of asthma symptoms and a lot of other diseases. Increasing amounts of evidence also suggest that longterm exposures to air pollution contribute to a high percentage of deaths between adults even children. Much more about the mechanisms that are involved with exacerbations induced by pollution needs to be understood, but oxidative stress and immune dysregulation are probably both involved.
We examined a part of data collected in a time series database in our close area and presented and visualized it by using a tool whereas very easy we can identify the limits and thresholds exceeded. Such overriding is causing alarm, especially for people with chronic diseases. The alarm can be a caution associated with the PHR and can inform the person and his medical practitioners for the situation, which can help in diagnose and prevention. Considering the implementation of similar solutions for the hardware used for gathering data and acknowledge the necessity of having that data available for calculations, machine learning methods can be used to provide a prediction, precision, and great presentation of the patient health status. 7
Part of the work presented in this paper has been carried out in the framework of the project “Cross-border initiative for integrated health and social services promoting safe ageing, early prevention and independent living for all (Cross4all)”, which is implemented in the context of the INTERREG IPA Cross Border Cooperation Programme CCI 2014 TC 16 I5CB 009 and co-funded by the European Union and national funds of the participating countries.