=Paper= {{Paper |id=Vol-2486/icaiw_wdea_6 |storemode=property |title=Modeling Air Quality and Cancer Incidences in Proximity to Hazardous Waste and Incineration Treatment Areas |pdfUrl=https://ceur-ws.org/Vol-2486/icaiw_wdea_6.pdf |volume=Vol-2486 |authors=Miriam Ugarte Querejeta,Ricardo S. Alonso |dblpUrl=https://dblp.org/rec/conf/icai2/QuerejetaA19 }} ==Modeling Air Quality and Cancer Incidences in Proximity to Hazardous Waste and Incineration Treatment Areas== https://ceur-ws.org/Vol-2486/icaiw_wdea_6.pdf

Modeling Air Quality and Cancer Incidences in
Proximity to Hazardous Waste and Incineration
Treatment Areas

Miriam Ugarte Querejeta and Ricardo S. Alonso
1
International University of La Rioja, Av. de la Paz, 137 26006 Logroño, Spain
miriam.ugarte@estudiante.unir.net,ricardoserafin.alonso@unir.net
2
BISITE Research Group, University of Salamanca, Edificio Multiusos I+D+i, Calle
Espejo 2, 37007 Salamanca, Spain
ralorin@usal.es

Abstract. This study analyzes the impact on human exposure and the
air quality in the vicinity to hazardous waste and incineration treatment
areas. Such an industry produces pollutant emissions that can be danger-
ous for the human health and the environment. Thus, various techniques
have been studied in order to model the relationship between the prox-
imity to these industrial plants, cancer incidences, and the air quality.
On the one hand, logistic regressions were carried out by having the dis-
tance as a categorical variable. On the other hand, variable where GAM
models were performed. The air quality parameters P M10 and N O2 are
higher in proximity to industrial areas according to both techniques,
whereas O3 happens to be lower. Regarding the incidences of cancer,
logistic regressions show that the incidences are higher in proximity to
certain industrial plants. However, there is no clear conclusion according
to the GAM models.

Keywords: air quality · cancer incidence · GAM models · logistic re-
gressions · pollutants.

1 Introduction

Incineration plants often treat and generate hazardous waste and are considered
one of the major sources of pollutant emissions such as dioxins and furans [7].
The International Agency for Research on Cancer (IARC) has classified dioxins
and furans as carcinogenic hazardous to humans [5] and there are many case
studies about the impact of the exposure to hazardous substances on the human
health, e.g. Seveso disaster in Italy [18,24,10].
The Basque country is a region in the north of Spain that suffers a high con-
centration of heavy industry and will be used as a case study. Various statistical
studies have been carried out over the past years to analyze the impact on human
health and environment in exposure to hazardous waste and incineration plants.
Especially, techniques such as linear models, logistic models, and Generalized
Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0)
2019 ICAI Workshops, pp. 108–122, 2019.
Modeling Air Quality and Cancer Incidences 109

Additive models have been studied in order to assess the air quality and cancer
risk in the Basque Country (Spain).
The main objective of this study is to analyze the air quality and cancer
incidence in proximity to hazardous waste and incineration treatment areas. On
the one hand, the impact of pollutants on human health and environment is
described. On the other hand, common data analysis techniques used in other
studies have been analyzed. Section 3 Focuses on data analysis techniques of
similar studies applied to the air quality and cancer risk. Thus, this study will
be focused in logistic regression models and generalized additive models. Section
4 Is the body of the study where it contains the whole data analysis procedure:
data collection, data selection and transformation, and data analysis and results.
The techniques described on the third section will be applied to analyze the
relationship between the cancer incidence and the proximity to hazardous waste
sources and also the air quality in areas close to the industrial plants. Finally,
the conclusions of the analyses and future work will be described in Section 5.

2 Problem description
Incineration plants are considered one of the main waste treatment systems in
many countries [21]. The incineration of hazardous waste produces highly toxic
pollutants such as dioxins and dioxin-like compounds that are environmentally
persistent and have the ability to bio-accumulate [1]. Waste incineration also
can generate heavy metals such as cadmium, mercury and lead and acid gases,
among others. Some of these substances are considered carcinogenic to humans
according to the International Agency for Research on Cancer (IARC) [5].

2.1 Impact of pollutants on human health and environment
The population is exposed to chemical substances by inhalation of polluted air
in its vicinity or consumption of local agricultural products that have been con-
taminated along the food chain [17].
Furthermore, emissions of hazardous waste processes are transformed into
gases, contaminated water, ash, and slag. Substances such as sulfur dioxide
(SO2 ) and nitrogen dioxide (N O2 ) are released into the environment as air,
water, and soil [22]. The emissions of these polluting substances may contain
adverse impacts on the respiratory health of the human [11,6].
The International legislation (Kiev Protocol on Pollutant Release and Trans-
fer Registers) [2], the European Pollutant Release and Transfer Register Regu-
lation (E-PRTR) [3] and the National Regulations (e.g., Spanish Royal Decree
508/2007 [4]) established a norm in order to regulate the emissions of pollutants
and register the inventory of pollutant releases to air, water, and soil.

2.2 Common data analysis techniques
This section explains the techniques and models carried out by previous studies
to solve similar statistical problems.
110 M. Ugarte et al.

A study carried out in Madrid (Spain) [14] analyzed the cancer mortality
in cities close to incinerators and hazardous waste treatment facilities. A higher
cancer mortality was observed in the vicinity of industrial facilities and especially
to incinerators. Two statistical approaches based on relative log-linear models
were used in order to assess the relative risk of mortality: a Bayesian conditional
auto regressive model (BYM) and a combined Poisson regression model. Both
models showed a significant risk in cancer mortality in the vicinity.
Another study investigated soft tissue sarcomas and non-Hodgkin lymphomas
in the vicinity of the Municipal Solid Waste (MSU) incinerator in France that
emitted high levels of dioxins. It was observed an increase of 44% increase in soft
tissue incidences and an increase of 27% incidences of non-Hodgkin lymphomas
within areas near the incinerator. On the one hand, clustering was performed to
determine groups within the area. On the other hand, space-time scan statistic
method was used to scan multiple data sets in order to look for clusters and
evaluate them [23].
Another similar study used Poisson distribution to analyze the incidences
of cancer in the vicinity of an incinerator, an oil refinery plant and a waste
disposal plant in Rome (Italy). Standardized mortality ratio (SMR) with two
95% confidence intervals were carried out by Poisson distributions [19].
A study analyzed the relationship between the risk of breast cancer and the
proximity to industrial plants classified by industrial activities and emissions in
Spain. Logistic regressions were used to estimate the Odds Ratio (OR) and 95%
confidence intervals of the distance categorized by the proximity to the industrial
plant (from 1km to 3km). The results demonstrated a possible increase of risk
of breast cancer in women living near certain industrial plants [13].
It exists another study that analyzed the relationship between the air quality
particle levels (P M10 and P M2.5 ), the meteorological conditions and the traffic.
The analysis was carried out by linear regressions and path analysis. The results
showed that the weather condition affects the air quality particles P M10 and
P M2.5 in open areas. On the other hand, the results showed that the traffic flow
has a direct effect in covered areas. The path analysis was more precise than the
linear regression and had a better fit for the study [20].
Zero-inflated regression model was used on a study about the depression
influencing factors in a large-scale population survey. The zero-inflated negative
binomial was demonstrated to be a good model for the depression factors on a
survey type study [25].

3 Data analysis techniques applied to air quality and
cancer risk

The aim of this study is to analyze the correlation of different type of tumors
and the air quality in proximity to incinerators and hazardous waste treatment
plants. The proximity has been considered a categorical variable and also as a
continuous numerical variable. Therefore, logistic regressions were applied for
Modeling Air Quality and Cancer Incidences 111

the first case study, whereas Generalized Additive Models have been studied on
the second case based on recent studies on the field.

3.1 Logistic Regression
An epidemiology study analyzed the association between the air pollution due
to heavy industry and lung and respiratory system problems in school chil-
dren. A cross-sectional study was conducted among children in the vicinity to a
heavy industrial area. Linear and logistic regressions were used to carry out the
relationship between the air quality parameters (P M2.5 , N OX ) and lung and
respiratory symptoms. The results concluded that the exposure to P M2.5 and
N OX was associated with children having lung function problems. The exposure
to P M2.5 was also associated with children suffering dry cough symptoms [9].
The Seveso disaster study was conducted to investigate the relationship be-
tween the air pollution and lung cancer and logistic regressions were used to
assess the relative risk of lung cancer in the vicinity of the incinerator [8].
Logistic regressions estimate the parameters of a logistic model. In this study,
the cities are classified into two categories according to their distance to the
industrial plants bearing the distance of 5km as a threshold [12,14]. Thus, cities
with a distance less than or equal to 5 km to the industrial plants have been
categorized as neighbouring cities and cities with a distance higher than 5 km
as distant cities. This theorem will perform the comparison of the two observed
categories (neighboring cities and distant cities) with respect to a numerical
variable (incidences of cancer and air quality).
Logistic regressions have been performed to assess the relationship between
the variables. The categorical value is the dependent variable X of the logistic
model and will be binomial in this case: neighbouring city and distant city. The
independent variable Y is the predictor and can be a continuous value such as
the incidences of cancer and the air quality.

Y ∼= B(N, f (Xβ)) (1)

3.2 Generalized Additive Model
A study conducted the assessment of cancer mortality in the vicinity of urban
solid waste incinerators carried out different statistical techniques to evaluate the
association between the risk of cancer and the proximity to incinerators. Tech-
niques such as Poisson regression, general additive models (GAM) and Bayesian
hierarchical analysis were used [15].
GAM models were also used to analyze the effects of each pollutant and
acute respiratory disease in children. GAM models were performed by applying
the quasi-Poisson regression. The study observed that there is an association
between exposure to P M10 , N O2 and SO2 pollutants and a greater number of
cases of acute respiratory disease in minors [26].
The Generalized Additive Model (GAM) is an extension of the Generalized
Linear Model (GLM). The main idea is to replace the linear component of the
112 M. Ugarte et al.

model by an additive component. GAM models are formed by the sum of smooth
functions (splines) or polynomials fi (Xi ). The purpose is to adjust the smooth
non-linear functions into predictor variables Xi to explain the relationship be-
tween the dependent variables Y and the predictor variables Xi [16].

Y = f1 (X1 ) + f2 (X2 ) + ... + fp (Xp ) + (2)

In this case, the dependent variable will be the incidences of cancer or the
air quality and the independent variables (predictors) will be the distance to the
industrial plant (dist), the distance to the closest industrial plant (dist min)
and the number of industrial plants per region (n) for each group of tumor, air
quality parameter and industrial plant.

4 Experiments and results

R is a programming language widely used for statistical analysis. R offers exten-
sive packages and libraries for machine learning and also statistical model pack-
ages which results ideal for this study. Rstudio is the integrated development
environment for R and it will be used in this study to carry out the following
tasks:

– Data collection
– Data selection and transformation
– Data Analysis and results

4.1 Data collection

The first step is to collect all the data from different sources and to restructure
into a common format or data frames in this case. The following data will be
retrieved:

Air quality data Air quality data has been obtained through the Air Quality
Control Network of the Basque Government. The air quality is daily measured
by P M10 , N O2 and O3 parameters. Parameters are classified into the following
categories by the Air Quality Index (IQA): Very low, Low, Moderate, High and
Very high. Thus, the daily Air Quality Index data of each municipality in 2017
has been retrieved.

Industrial plants data The Spanish National Regulation of pollutants con-
tains the inventory of all the industrial plants of hazardous waste treatment and
waste incinerators. It reports yearly emissions of all industrial activities to com-
ply with the regulation. Thus, hazardous waste treatment industrial plants of
the Basque Country that carried out industrial activities within 2007 and 2016
have been retrieved, in total 109 industrial plants have been studied.
Modeling Air Quality and Cancer Incidences 113

Epidemic data Epidemic data has been obtained by the Public Health Direc-
tion of the Basque Government under privacy and data protection terms. The
epidemic data is classified by gender, age-range, and city of the diagnosed person.
The incidences are divided into 26 groups of different types of tumors according
to the International International Classification of Diseases ICD-10.

Population data

4.2 Data Selection and transformation
On one hand, the parameters of interest of each data frame will be selected and
common parameters such as the geoposition will be identified in order to be able
to join them. On the other hand, one of the main tasks is to calculate the distance
between the industrial plants and cities where the incidences of cancer and air
quality measurements have been retrieved. The distance will be calculated by
the distGeo function of R as denoted in the following equation:

setDT (df1 [, dis := distGeo(matrix(c(df2 $lon.x, df2 $lat.x), ncol = 2),
(3)
matrix(c(df1 $lon.y, df1 $lat.y), ncol = 2))]

4.3 Data Analysis and results
On the one hand, correlations will be carried out to check if there is any signif-
icant relationship between the variables. On the other hand, regressions will be
performed to model the relationships. t-student and ANOVA metrics are used to
assess the results. t-student evaluates whether the difference observed between
the means of two groups is significant in respect to the hypothesis that has been
defined. ANOVA tests evaluate if there is any significant relationship between
different populations by comparing the variance of the population.

Logistic regression t-student statistic will be used to carry out the analysis
in respect to the null hypothesis for its numerical variable.
Having incidences of cancer as a numerical variable:

– H0 the mean of incidences of cancer in the neighboring cities is the same as
the mean of incidences in the distant cities (null hypothesis).
– H1 the mean of incidences of cancer in the neighboring cities is different
than the mean of incidences in the distant cities (alternative hypothesis).

Having the air quality as a numerical variable:

– H0 the mean of the air quality in the neighboring cities is the same as the
mean of the air quality in the distant cities (null hypothesis).
– H1 the mean of the air quality in the neighboring cities is different than the
mean of the air quality in the distant cities (alternative hypothesis).
114 M. Ugarte et al.

p-value is used to weigh the strength of the evidence. The null hypothesis
will be rejected with an interval confidence of 99% if there is a 0.01 probability
of rejecting this hypothesis. To completely reject the null hypothesis p-value has
to be less than α = 0.01 and F value greater than the F-critical.

qf (1 − α, df1 , df2 ) (4)

glm (generalized linear model) functions of the mgcv library in R are used
to calculate logistic regression models.

Incidences of cancer Incidences of cancer are modeled by logistic regressions
in order to assess the relationship between the categorical distance and the in-
cidences.

glm(km ∼ incidences, data = dftumour , f amily = “binomial”) (5)

The logistic regression shows that there are more incidences of cancer in the
vicinity to industrial plants with a p-value < 0.01. Cities that are close to the
industrial plant with NationalID 3694 have more incidences of cancer of group
20 as shown in Fig. 1:

Fig. 1. Logistic regression of cancer incidences, NationalID=3694 and group of can-
cer=20
Modeling Air Quality and Cancer Incidences 115

P M10 The air quality index P M10 index is modeled by logistic regression to
assess the relationship between the categorical distance and the index.

glm(km ∼ P M10 , data = dfP M10 , f amily = “binomial”) (6)
The logistic regression shows that there is a higher probability of being in
a distant city to the industrial plant when the value of the parameter of P M10
is low. On the same way, the probability of being in a neighboring city to the
industrial plant will be greater when the value of P M10 is high as seen in Figure
2.

Fig. 2. Logistic regression of P M10 , NationalId =3698 and month=April

N O2 The air quality index N O2 index is modeled by logistic regression to assess
the relationship between the categorical distance and the index.

glm(km ∼ N O2 , data = dfN O2 , f amily = “binomial”) (7)
There is a higher probability of being in a neighboring city to the industrial
plant when the value of the N O2 parameter is greater as seen in Fig. 3.

O3 The air quality index O3 index is modeled by logistic regression in order to
assess the relationship between the categorical distance and the index.

glm(km ∼ O3 , data = dfO3 , f amily = “binomial”) (8)
116 M. Ugarte et al.

Fig. 3. Logistic regression of N O2 , NationalId =3702 and month=April

There is a higher probability of being in a neighboring city to the industrial
plant when the value of the O3 parameter is low as seen in Fig.4.

Generalized Additive Model The linear predictor is not forced to be linear
in this case and it is constructed by the sum of smooth functions called splines.
The variable can be continuous, categorical, linear, data series, etc. Thus, gam
functions of the mgcv library in R are used to calculate the generalized additive
models.

Incidences of cancer Incidences of cancer are modeled by GAM so as to
assess the relationship between the distance and the incidences.

gam(incidences ∼ s(n, k = 20) + s(distmin , k = 20) + s(dist, k = 20)) (9)

The GAM model varies with the industrial plant and the group of tumor and
there is no clear pattern between the incidences and the distance to the industrial
plants. The model can not be generalized as there might be areas affected by
other factors as seen in Fig. 5.

P M10 The air quality index P M10 is modeled by GAM to assess the relationship
between the distance and the index.
Modeling Air Quality and Cancer Incidences 117

Fig. 4. Logistic regression of O3 , NationalId =4682 and month=August

Fig. 5. GAM model of cancer incidences, NationalID=3701 and group of cancer =16
118 M. Ugarte et al.

gam(P M10 ∼ s(n, k = 20) + s(distmin , k = 20) + s(dist, k = 20)) (10)

Regarding the GAM model, the value of the parameter P M10 is higher (worse
AQI) in the vicinity of the industrial plant and it decreases and improves with
the distance as described in Fig. 6.

Fig. 6. GAM model of P M10 , NationalId =3643 and month=April

N O2 The air quality index N O2 is modeled by GAM in order to assess the
relationship between the distance and the index.

gam(N O2 ∼ s(n, k = 20) + s(distmin , k = 20) + s(dist, k = 20)) (11)

The parameter N O2 is greater in the vicinity of the industrial plant and it
decreases with the distance according to the GAM model as seen in Fig. 7.

O3 The air quality index O3 is modeled by GAM to assess the relationship
between the distance and the index.

gam(O3 ∼ s(n, k = 20) + s(distmin , k = 20) + s(dist, k = 20)) (12)
Modeling Air Quality and Cancer Incidences 119

Fig. 7. GAM model of N O2 , NationalId =7189 and month=October

The following Figure shows the GAM model for the NationalId 5916 and the
parameter O3 in September. The value of O3 is increasing linearly until being
60 km far from the industrial plant. At this point, the increase of O3 parameter
gets smaller as described in Fig. 8.

5 Conclusions and Future Work

Logistic regression model results show a significant association (p < 0.01) be-
tween cancer incidences and the proximity to industrial plants of hazardous waste
treatment. Logistic regression models also show that P M10 and N O2 values are
higher in the proximity to certain industrial plants with p < 0.01. However, O3
levels happen to be lower.
GAM model does not fit well to represent the association between incidences
of cancer as the model changes by the type of tumor and the industrial plant. The
results can not be generalized by GAM models as certain tumors may be biased
by other factors that have not taken into account. For example, lung cancer
incidences could be biased by tobacco consumption. However, GAM models have
demonstrated to be a good fit to represent the relationship between air quality
and the proximity to industrial plants. P M10 and N O2 levels are higher in the
vicinity to these industrial plants whereas O3 levels are lower. GAM models seem
to fit better than logistic regressions to model the behavior of the air quality.
120 M. Ugarte et al.

Fig. 8. GAM model of O3 , NationalId =5916 and month=September

Atmospheric emissions may be conditioned by meteorological variables such
as precipitation, temperature, wind, etc. and thus, it would be interesting to
study the impact of weather conditions as a future work. Also, industrial plants
could be classified by the activities carried out and the emitted substances in
order to go deeper in the analysis and find a relationship with the substances
that are emitted.

Acknowledgements This work has been supported by the International Uni-
versity of La Rioja (Spain).

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