environments
Article
Economic and Life Cycle Analysis of Passive and Active
Monitoring of Ozone for Forest Protection
Elisa Carrari 1,2 , Alessandra De Marco 3, * , Andrea Laschi 4 , Ovidiu Badea 5,6 , Laurence Dalstein-Richier 7 ,
Silvano Fares 8 , Stefan Leca 5 , Enrico Marchi 2 , Pierre Sicard 9 , Ionel Popa 5 , Yasutomo Hoshika 1 ,
Alessandro Materassi 8 , Giacomo Pallante 3 , Diana Pitar 5 and Elena Paoletti 1
1
2
3
4
5
6
7
8
9
*
Citation: Carrari, E.; De Marco, A.;
Laschi, A.; Badea, O.; Dalstein-Richier,
L.; Fares, S.; Leca, S.; Marchi, E.;
Sicard, P.; Popa, I.; et al. Economic
and Life Cycle Analysis of Passive
and Active Monitoring of Ozone for
Forest Protection. Environments 2021,
8, 104. https://doi.org/10.3390/
environments8100104
Academic Editor: Yu-Pin Lin
Received: 26 August 2021
Accepted: 1 October 2021
Published: 9 October 2021
IRET-CNR, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy; elisa.carrari@cnr.it (E.C.);
yasutomo.hoshika@cnr.it (Y.H.); elena.paoletti@cnr.it (E.P.)
DAGRI-UNIFI, P. le Cascine 28, 50144 Firenze, Italy; enrico.marchi@unifi.it
ENEA, SSPT–PVS, Via Anguillarese 301, 00123 Rome, Italy; giacomo.pallante@enea.it
SAAF-UNIPA, Viale delle Scienze, ed. 4, 90128 Palermo, Italy; andrea.laschi@unipa.it
INCDS, 128 Eroilor Bulevard, 077030 Voluntari, Romania; obadea@icas.ro (O.B.); stefan.leca@icas.ro (S.L.);
popaicas@gmail.com (I.P.); dianamicasm@gmail.com (D.P.)
Faculty of Silviculture and Forest Engineering, Transilvania University, 1, Ludwig van Beethoven Str.,
500123 Braşov, Romania
GIEFS, 69 Avenue des Hespérides, 06300 Nice, France; ldalstein-richier@departement06.fr
IBE-CNR, 00185 Rome, Italy; silvano.fares@cnr.it (S.F.); alessandro.materassi@cnr.it (A.M.)
ARGANS, 260 route du Pin Montard, 06410 Biot, France; psicard@argans.eu
Correspondence: alessandra.demarco@enea.it; Tel.: +3-93-409-749-972
Abstract: At forest sites, phytotoxic tropospheric ozone (O3 ) can be monitored with continuously
operating, active monitors (AM) or passive, cumulative samplers (PM). For the first time, we present
evidence that the sustainability of active monitoring is better than that of passive sensors, as the
environmental, economic, and social costs are usually lower in the former than in the latter. By using
data collected in the field, environmental, social, and economic costs were analyzed. The study
considered monitoring sites at three distances from a control station in Italy (30, 400, and 750 km),
two forest types (deciduous and Mediterranean evergreen), and three time windows (5, 10, and
20 years of monitoring). AM resulted in more convenience than PM, even after 5 years, in terms of
O3 depletion, global warming, and photochemical O3 creation potential, suggesting that passive
monitoring of ozone is not environmentally sustainable, especially for long time periods. AM led to
savings ranging from a minimum of EUR 9650 in 5 years up to EUR 94,796 in 20 years in evergreen
forests. The resulting social cost of PM was always higher than that of AM. The present evaluation
will help in the decision process for the set-up of long-term forest monitoring sites dedicated to the
protection of forests from O3 .
Keywords: tropospheric ozone detection; forests protection; LCA analysis; sustainability; CO2 emissions
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affiliations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1. Introduction
Sustainability is a holistic approach that considers ecological, social, and economic
dimensions, recognizing that all must be considered together [1]. Sustainability is usually
defined as the processes and actions through which humankind avoids the depletion of
natural resources in order to keep an ecological balance that does not allow the quality of
life of modern societies to decrease. Indeed, the evaluation of the sustainability of different
goods or services takes into account environmental, economic, and social impacts [2,3].
Environmental impact assessment includes the emissions into the environment related
with the extraction of the raw materials, manufacturing of the products, and resource
consumption [3,4]. The manufacturing, use, and disposal of a product involve a series
of inputs, in terms of energy and resources, which generates several outputs, in terms of
materials and emissions. These contribute to a wide range of impacts on ecosystems and
Environments 2021, 8, 104. https://doi.org/10.3390/environments8100104
https://www.mdpi.com/journal/environments
Environments 2021, 8, 104
2 of 20
human health, such as climate change, stratospheric ozone (O3 ) depletion, tropospheric O3
formation, eutrophication, acidification, toxicological stress on human health and ecosystems, depletion of resources, water use, land use, and noise [4–7]. Life cycle assessment
(LCA) is the internationally recognized methodology to evaluate the convenience of a good
or a service in terms of environmental sustainability (8). Thanks to this methodology, it is
possible to assess the entire life cycle of a product, process, or activity to identify, quantify,
and environmentally analyze all the inputs and outputs involved in the production, use,
and disposal of that product, process, or activity [8–11].
Forest monitoring is a crucial key step in the protection of forests from different
stressors related to air pollution and climate change [12–15]. Among the air pollutants,
tropospheric O3 is of primary interest for vegetation due to its elevated phytotoxicity, even
at ambient concentrations [16]. Indeed, O3 is recognized as a major concern for plant
health, as it impacts crop yield [17], forest growth [18,19], and biodiversity [20]. Ozone is a
secondary air pollutant formed in the atmosphere under sunlight from the oxidation of
the primary pollutants, nitrogen oxides and volatile organic compounds [21]. Ozone is
still a global problem for forest productivity, as highlighted by the analysis of present and
future global scenarios [22,23]. The exposure index for forest protection against negative
impacts of background O3 currently used in Europe is the concentration-based index
AOT40, defined as the accumulated O3 dose above 40 ppb during daylight hours over the
growing season, although a new index has been proposed as more appropriate, i.e., POD1,
defined as the phytotoxic O3 dose exceeding 1 nmol m−2 s−1 of stomatal uptake, cumulated
over daylight hours during the growing season [24,25]. Both indexes require hourly data
to be calculated.
At forest sites, tropospheric O3 can be monitored with either continuously operating, mechanical, real-time active monitors or passive, cumulative, total exposure samplers [26,27]. The passive system has been used since 2000 in Europe, e.g., at the Level II
forest sites of the ICP Forests network [28], while the active system is used at some ICP
Forests sites [29]. Passive samplers are characterized by uncertainties that reduce their
reliability [30,31], and low temporal resolution, from one week to one month, while POD1
and AOT40 require hourly data. This implies the need to apply functions to estimate hourly
concentrations, starting from weekly or biweekly data. Among different methods [31–34],
the ICP Forests manual recommends the use of the Loibl function [35–37] to estimate
hourly values. There are contrasting results, however, about the actual adequacy of this
function in nonhomogeneous territories [38]. The uncertainties in estimating POD1 by
passive sampling are discussed in [39], which tested the suitability of using aggregated
data instead of hourly data for PODY (POD with variable stomatal uptake threshold (Y))
calculations [39].
An assessment of the environmental impacts of the active and passive systems has
never been carried out, but can help evaluating the suitability of the two monitoring
methodologies. It is even important to consider the economic consequences of these
alternative systems, i.e., determine the cost-effectiveness of the alternative investments [40].
Financial limitations, especially in ecological programs, require a clear identification of
costs [41], and the active technique is considered more expensive; active monitors are
expensive and require electricity and a safe climate-controlled shelter for effective operation,
while passive samplers are inexpensive, easy to use, and require no electricity [42]. At
remote sites, the availability of power supply is often limited, and this has been a major
limitation to the use of the active O3 monitoring so far. Nowadays, however, solar panels
have relatively low cost and their use, supported by batteries, is a widely adopted solution
in remote meteorological stations. In addition, less trips to the forests are required for data
collection thanks to remote connection via GPRS for the active monitoring system than
for the passive approach. Hence, a comparison of the explicit costs in a defined period is
needed to evaluate the cost-effectiveness of the two systems.
To complete the evaluation of the monitoring system sustainability, we calculated the
social costs considered here as the social cost of carbon (SCC), which is among the most
Environments 2021, 8, 104
3 of 20
important pillars in the economics of climate change [43]. SCC is the global cost caused
by an additional ton of carbon dioxide (CO2 ) emissions or its equivalent [44]; for this
reason, it is often combined with LCA studies [45,46]. Indeed, estimates of SCC are used
to evaluate climate change policies and cost-benefit analysis of GHG emission reduction
projects [43,47].
The aim of the present study is to evaluate, for the first time, the sustainability of active
and passive O3 monitoring at forest plots, analyzing the data in terms of environmental,
economic, and social costs. We hypothesized that (1) the expected higher costs of active
monitoring are compensated over time, and, thus, we performed our analyses over different
time windows (i.e., 5, 10, and 20 years of monitoring); (2) differences depend on the
seasonal duration of the monitoring, and, thus, we compared two sites dominated by either
deciduous (April-to-September growing season) or Mediterranean evergreen (year-long)
trees; (3) the distance between monitoring sites and the control station could be relevant in
terms of costs, and, thus, we compared sites at 30, 400, and 750 km from the control station.
2. Materials and Methods
2.1. Description of the Two Monitoring Methods
ICP Forests establishes a specific protocol for passive O3 monitoring (PM), but it does
not recommend a specific category of samplers. Hence, this study followed the ICP Forests
manual [13] and applied the protocol to the two most common passive sampler types used
by scientists, i.e., Institutet for Vatten-och Luftvadsforskning (IVL) [48] and Ogawa & Co.,
Inc., Gifu, Japan and types [49,50].
The passive samplers are filters without any system of air control. Both Ogawa and
IVL samplers must be installed at forest sites at a height of about two meters above ground,
supported by a steel bar planted into the ground (Figure 1a A1). At least two replicates
(Figure 1a A2) must be simultaneously exposed at each site and located in protective
shelters (Figure 1a A3) in order to reduce eventual environmental disturbances. According
to the European protocol [13], the sampling is carried out on a 2-week basis and covers
the period 1 April–30 September. For Mediterranean conditions (evergreen species), it is
recommended to monitor the whole year (1 January–31 December). In the present analysis,
data collection in the evergreen Mediterranean forest was conducted every month in the
six months from October to March (when O3 concentrations are expected to be lower),
and every two weeks in the growing season from April to September (Table 1). Transport
and storage of the passive samplers before and after exposure may have an influence on
the chemical analysis; thus, the protocol described in the ICP Forests manual [13] must
be strictly followed. For quality control, at least four blank samplers per season should
be included in the standard transport and storage procedures. After sampler collection,
filters are immersed into extraction solutions placed in clean plastic vials. The vials are then
stored in a refrigerator at 4 ◦ C until analysis made by ion chromatography. To prepare the
filters for analysis, the following equipment and supplies are needed: calibrated automatic
dispensing pipette (5 mL); forceps, not serrated, sharp with curved tip; IC vials (0.5 mL for
Dionex auto-sampler); caps for IC vials; syringes, 3 mL disposable; Millex-LCR13 syringe
filters [51].
In the active monitoring (AM), O3 is measured by means of an active sensor. We
used the Model 106 L (2B Technologies, Inc., Boulder, CO, USA; Figure 1b B1), which is
a common active O3 monitor in forest AM [51]. Ozone monitors must be protected by
solar radiation with a specific screen (Figure 1b B2). Data are recorded by a data logger
(Campbell scientific in our case, Figure 1b B5). The data acquisition interval is 10 s and the
average is stored every hour. Thanks to a GPR connection, data are transmitted to a file
transfer protocol (FTP) server via a GPRS modem (Figure 1b B5). In AM, air is sampled
through the sample inlet (Figure 1b B4), passing through a particle filter (Figure 1b B3) that
needs to be replaced every 3 months. A monitor calibration is required once per year. In
addition, battery replacement is required every 4 years. Power supply is assured by solar
Environments 2021, 8, 104
4 of 20
panels (Figure 1b B6) or mains, when present, and backup batteries to be used in case of
power failure (Figure 1b B7).
Figure 1. Schematic representation of passive (a) and active (b) ozone monitoring stations. The
passive station shows an IVL O3 sampler, the active station shows a model 106-L (2B Technologies,
Inc., Boulder, CO, USA) O3 sensor.
Table 1. Number of trips and work time (WT) for each trip from the central station to the forest site for two case studies at a
similar distance from the station, i.e., evergreen Mediterranean (EF) and deciduous (DF) forests, and three time windows,
i.e., 5, 10, and 20 years of monitoring. Simulations are based on the data collected during the 5-year project MOTTLES [29].
Passive Monitoring IVL/OGAWA
Items
Deciduous forest
Installation
Maintenance
activity
Extraordinary
maintenance
Data collection
Evergreen forest
Installation
Maintenance
activity
Extraordinary
maintenance
Data collection
5 Years
10 Years
Active Monitoring
20 Years
5 Years
10 Years
20 Years
N.
Trips
WT
(h)
N.
Trips
WT
(h)
N.
Trips
WT
(h)
N.
Trips
WT
(h)
N.
Trips
WT
(h)
N.
Trips
WT
(h)
1
14.25
1
14.25
1
14.25
1
28.5
1
28.5
1
28.5
0
0
0
0
1
14.25
20
28.5
40
28.5
80
28.5
1
28.5
2
28.5
5
28.5
/
/
/
60
14.25
120
14.25
240
14.25
1
14.25
1
14.25
1
14.25
1
28.5
1
28.5
1
28.5
0
0
0
0
1
14.25
20
28.5
40
28.5
80
28.5
1
28.5
2
28.5
5
28.5
/
90
/
14.25
180
/
14.25
360
14.25
/
/
/
/
/
/
2.2. Analyzed Factors
To test our hypotheses, the environmental, monetary, and social analyses of the
two PM and AM systems were implemented by 2 case studies (deciduous forest—DF
and evergreen Mediterranean forest—EF with O3 monitoring over the growing seasons,
i.e., April to September and all year round, respectively) and 3 time windows (5, 10,
and 20 years of monitoring duration). The time windows were selected as low (5 years),
medium (10 years), and long-term (20 years) on the basis of the supposed average life
of the solar panel needed for power supply in the AM. To have comparable results, the
DF and EF case sites were selected at a fixed distance from the control station (a research
center in Italy). The distance of the two forest sites from the control station was 335 km for
Environments 2021, 8, 104
5 of 20
VEN1 (DF site in Pian del Cansiglio (Belluno), dominated by Fagus sylvatica) and 355 km
for CPZ1 (EF site in Castel Porziano (Rome), dominated by Quercus ilex). The distance is
representative because the average distance of all the 17 monitoring sites of the MOTTLES
network [29] from the control station was 400 km. Therefore, 400 km was considered as an
average distance between research center and forest plot, with a maximum and minimum
distance of 700 and 30 km, respectively. All data on materials, personnel, and travels used
for LCA, economic, and social analysis corresponded to real data collected for VEN1 and
CPZ1. Within AM, the cost in DF did not differ from the cost in EF (and, thus, only one
value was shown).
2.2.1. Definition of System Boundaries and Functional Unit
A system of pollutant monitoring is normally built to provide information, generally
related with a series of values referred to a specific time frame (e.g., average concentration
per day, maximum and minimum per hour, etc.). AM does not need laboratory analysis,
while PM does. Materials, energy, and emissions related to the activities carried out to
finalize O3 monitoring, in both AM and PM, were included in the LCA analysis. In particular, the system boundaries for both the analyzed monitoring systems included production,
installation, and maintenance of monitoring stations (including sensors, energy suppliers,
fence, etc.), energy and materials used during monitoring periods, and maintenance and
data collection activities. Travel of technicians from the research center to the plot site was
also considered. In PM, despite the simpler composition of the remote station, many travels
are required to collect the filters in the field. Travel needs are reduced with AM, given that
data collection is done by remote. The described information was classified as primary
data, being obtained by interviews to producers of monitoring systems and to researchers
and technicians who work in O3 monitoring. The background information, such as raw
material extraction, machinery, and plastics production, and electricity production and
supply, were collected by internationally recognized databases. The use of information
provided by the system monitoring can be very different depending on the activities carried
out by research centers, public health laboratories, etc. For this reason, the analysis made
in this study stops when the information on O3 concentration is available for the final user.
Moreover, the disposal of the monitoring stations was not considered due to the lack of
real experience in this field. Finally, in this study, a cradle-to-gate analysis was carried out.
All the identified inputs and outputs have to be referred to a functional unit, which
allows the comparison between the two systems. In this study, the functional unit was
defined as: “the biweekly supply of atmospheric O3 concentrations (ppb) over one year”.
It is important to underline that the chosen functional unit is based on the capacity of the
passive sampler to supply information every two weeks. The AM gives more frequent and
detailed information than the PM, but the comparison between the two systems is suitable
because they are considered as alternatives by scientists [26,29,52,53].
2.2.2. Definition of Subsystems
To implement the inventory (LCI—Life Cycle Inventory) (International Organization
for Standardization, 2006) and to clearly identify the inputs and outputs in the main phases
of life cycle, three subsystems were identified and defined in the two flow charts and system
boundaries (Figures 2 and 3): (i) the production of O3 monitors (AM) or samplers (PM);
(ii) the on-site preparation, installation, operation, and maintenance of the monitoring
system; and (iii) data collection and (only for PM) laboratory analysis.
Environments 2021, 8, 104
6 of 20
Figure 2. Flow chart and system boundaries for active ozone monitoring in a forest.
Figure 3. Flow chart and system boundaries for passive ozone monitoring in a forest.
→ Subsystem 1—Ozone monitor production
In this subsystem, the main processes involved in O3 monitor production were identified, and reliable data of materials and energy consumptions related with their production
were collected, mainly by interviews to producers and analysis of the products. The main
Environments 2021, 8, 104
7 of 20
raw materials for monitors were aluminum and steel. Active systems were more complex
than passive ones, with a higher weight and more structures and devices (such as solar panels, battery). In passive systems, the plastic sensor was not considered (Figure 1a A2) due
to the difficulties in understanding the type of material used and the production process.
This missing information was considered as negligible due to the minimum weight and
related inputs and emissions. In active systems, the production processes and materials
used in the analysis were assimilated to the most suitable components/materials available
on databases.
→ Subsystem 2—Preparation, installation, operation, and on-site maintenance of the
monitoring systems
In this subsystem, the devices considered in subsystem 1 were the input, and were
added to the materials needed to install the remote monitoring station. Active systems
require electricity to work; for this reason, a photovoltaic panel with all the additional
components needed to guarantee energy supply to the active monitor (inverter, battery,
cables, supports, etc.) were considered. These inputs were not applied in the passive
system, which does not require energy in the field. In this subsystem, the travels (by car)
of technicians and researchers needed to collect samples (passive system) and to make
maintenance interventions were also considered (Table 1). The type of forest influenced the
number of travels in PM, as the number of samples to be collected in one year varied and
was higher in EF, while it did not affect AM because the number of travels for maintenance
was not related with the length of the data collection period.
→ Subsystem 3—Data supply
In this subsystem, all the processes, from information collected in the field to the raw
data available for stakeholders, were included. This subsystem was important mainly for
passive systems, because it included laboratory analysis of passive samplers. Data about
laboratory activities were obtained thanks to the available protocols and interviews with
technicians. Active samplers sent to the research center, calculation of the O3 concentration
data, and inputs related to the data transfer system were included in Subsystem 2.
For each subsystem, primary data were collected by interviews to producers of monitoring systems and to researchers and technicians who work in O3 monitoring. The
background information, such as raw material extraction, machinery and plastics production, and electricity production and supply, were collected by internationally recognized
databases (GaBI, Ecoinvent). An overall summary of the LCI, including background
processes from databases, was reported for both PM (Table 2) and AM (Table 3).
2.2.3. Life Cycle Impact Assessment (LCIA)
Data collected in LCI were implemented by software (GaBi version 7.3), and the
environmental assessment LCIA was conducted by characterization factors included in
CML 2001 (update January 2016). Six impact categories were analyzed: acidification
potential (AP), eutrophication potential (EP), global warming potential (GWP), human
toxicity potential (HTP), ozone layer depletion potential (ODP), photochemical ozone
creation potential (POCP). The results of LCIA were organized in two main categories
of emissions: (i) “travel” to the monitoring site and back for sample collection (PM) and
maintenance (both PM and AM), and (ii) “material”, which included all the components of
monitors (PM and AM) and the materials used in laboratory analysis.
2.3. Environmental Costs
The guidelines established by ISO 14,040 (International Organization for Standardization, 2006) were applied to compare the environmental profile of the two methods for
forest O3 monitoring through life cycle assessment (LCA). The implementation of a comparative LCA for two or more services requires the definition of comparable systems and a
suitable functional unit, which will be the term of comparison. In this study, the analysis
was applied to a service (O3 monitoring) that supplies a set of information (values of O3
Environments 2021, 8, 104
8 of 20
concentration) for a defined period. Following ISO standards, all the required phases were
implemented, including life cycle inventory (LCI) and life cycle impact assessment (LCIA)
(International Organization for Standardization, 2006). The two systems were analyzed
by considering the same input and output categories, and excluding eventual information
available for only one of the two systems.
Input
Amount
Unit
Travels by car
800
km
Gasoline
40.3
kg
Road allocation
0.6
Ecoinvent
quantity (ma)
Tire consumption
−0.059
kg
Road consumption
−0.010
kg
Brake consumption
−0.005
kg
Ordinary maintenance of car
0.005
n
Structure
10
kg
Welding
2
m
Production
0.01
kg
Electricity
2.36 × 10−4
MJ
Heat
0.00683
MJ
Lubricants
1.43 × 10−6
kg
10−5
kg
PP granulate
4.98 × 10−6
kg
Syringe production
0.0025
kg
Electricity
1.64 × 10−5
MJ
Heat
0.00171
MJ
Lubricants
3.58 × 10−7
kg
10−6
kg
1.25 × 10−6
kg
Test tube
Passive
sensors
Travel (installation and maintenance)
Table 2. Summarized inventory of inputs and outputs referred to the case study of ozone monitoring with passive sensors.
The initial acronyms in “applied processes” refer to geographic location: “RER” = Europe; “GLO” = global; “Row” = rest of
the world.
Syringe
Laboratory analysis
Waste recycling
Waste recycling
PP granulate
3.68 ×
9.23 ×
Applied Processes
RER: transport, passenger car, small size,
petrol, EURO 5, <u-so>. Ecoinvent 3.3
RoW: market for petrol, low-sulfur.
Ecoinvent 3.3
RoW: market for road. Ecoinvent 3.3
GLO: market for tire wear emissions.
Ecoinvent 3.3
GLO: market for road wear emissions.
Ecoinvent 3.3
GLO: market for brake wear emissions.
Ecoinvent 3.3
GLO: market for passenger car maintenance.
Ecoinvent 3.3
RoW: sheet rolling, chromium steel.
Ecoinvent 3.3
RoW:welding arc, steel.
Ecoinvent 3.3
RoW: extrusion production, plastic pipes
<u-so>. Ecoinvent 3.3
GLO: market group for electricity, high voltage.
Ecoinvent 3.3
Europe without Switzerland: market for heat,
district or industrial, other than natural gas.
Ecoinvent 3.3
GLO: market for lubricating oil. Ecoinvent 3.3
Europe without Switzerland: market for waste
plastic, mixture. Ecoinvent 3.3
GLO: market for polypropylene, granulate.
Ecoinvent 3.3
RoW: extrusion production, plastic pipes
<u-so>. Ecoinvent 3.3
GLO: market group for electricity, high voltage.
Ecoinvent 3.3
Europe without Switzerland: market for heat,
district or industrial, other than natural gas.
Ecoinvent 3.3
GLO: market for lubricating oil. Ecoinvent 3.3
Europe without Switzerland: market for waste
plastic, mixture. Ecoinvent 3.3
GLO: market for polypropylene, granulate.
Ecoinvent 3.3
Environments 2021, 8, 104
9 of 20
Table 2. Cont.
Filter for syringe
Input
Amount
Unit
Filter for syringe production
0.0005
kg
Electricity
1.53 × 10−5
MJ
Heat
0.00011
MJ
Lubricants
5.25 × 10−8
kg
10−5
kg
2.44 × 10−8
kg
Milli-Q water
22.58
kg
Sulfuric acid
0.0513
kg
Waste recycling
PP granulate
1.21 ×
Applied Processes
RoW: extrusion production, plastic film <u-so>.
Ecoinvent 3.3
GLO: market group for electricity, high voltage.
Ecoinvent 3.3
Europe without Switzerland: market for heat,
district or industrial, other than natural gas.
Ecoinvent 3.3
GLO: market for lubricating oil. Ecoinvent 3.3
Europe without Switzerland: market for waste
plastic, mixture. Ecoinvent 3.3
GLO: market for polyvinylidenchloride,
granulate. Ecoinvent 3.3
GLO: market for water, ultrapure.
Ecoinvent 3.3
GLO: market for sulfuric acid. Ecoinvent 3.3
Steel
Active monitor
Travel (installation and maintenance)
Table 3. Summarized inventory of inputs and outputs referred to the case study of ozone monitoring with active sensors.
The initial acronyms in “applied processes” refer to geographic location: “RER” = Europe; “GLO” = global; “Row” = rest of
the world.
Input
Amount
Unit
Travels by car
800
km
Gasoline
40.3
kg
Road allocation
0.6
Ecoinvent
quantity (ma)
Tire consumption
−0.059
kg
Road consumption
−0.010
kg
Brake consumption
−0.005
kg
Ordinary maintenance
of car
0.005
n
Steel extrusion
0.31
kg
Compressed air
0.09
m3
Finite element modeling
0.31
kg
Modeling machine
1.22 × 10−5
kg
Allocation working factory
1.42 × 10−10
Unit
0.31
kg
0.31
kg
Preparatory steel treatments
Applied Processes
RoW: transport, passenger car, small size,
petrol, EURO 5, <u-so>. Ecoinvent 3.3
RoW: market for petrol, low-sulfur.
Ecoinvent 3.3
RoW: market for road. Ecoinvent 3.3
GLO: market for tire wear emissions.
Ecoinvent 3.3
GLO: market for road wear emissions.
Ecoinvent 3.3
GLO: market for brake wear emissions.
Ecoinvent 3.3
GLO: market for passenger car maintenance.
Ecoinvent 3.3
RoW: impact extrusion of steel, cold, 1 stroke
<u-so>. Ecoinvent 3.3
GLO: market for compressed air, 700 kPa
gauge. Ecoinvent 3.3
GLO: market for impact extrusion of steel,
cold, 1 stroke. Ecoinvent 3.3
RoW: metal working machine production,
unspecified. Ecoinvent 3.3
GLO: market for metal working factory.
Ecoinvent 3.3
GLO: market for impact extrusion of steel,
cold, tempering. Ecoinvent 3.3
GLO: market for impact extrusion of steel,
cold, initial surface treatment. Ecoinvent 3.3
Environments 2021, 8, 104
10 of 20
Aluminum
Table 3. Cont.
Input
Amount
Unit
Aluminum extrusion
0.81
kg
Compressed air
0.235
m3
Finite element modeling
0.81
kg
Modeling machine
3.20 × 10−5
kg
Allocation working factory
3.71 × 10−10
Unità di
lavoro
Aluminum extrusion
0.81
kg
Preparatory aluminum
treatments
0.81
kg
0.81
kg
Applied Processes
RoW: impact extrusion of aluminium, 1 stroke
<u-so>. Ecoinvent 3.3
GLO: market for compressed air, 700 kPa
gauge. Ecoinvent 3.3
GLO: market for impact extrusion of
aluminum, deformation stroke. Ecoinvent 3.3
RoW: metal working machine production,
unspecified. Ecoinvent 3.3
GLO: market for metal working factory.
Ecoinvent 3.3
RoW: impact extrusion of aluminum, 1 stroke
<u-so>. Ecoinvent 3.3
GLO: market for impact extrusion of
aluminum, cold, tempering. Ecoinvent 3.3
GLO: market for impact extrusion of
aluminum, cold, initial surface treatment.
Ecoinvent 3.3
2.4. Economic Costs
In order to compare the costs of AM, PM-Ogawa, and PM-IVL, we defined the following three cost categories:
- Materials. We considered the monitor devices and the complementary material
(structures, power supply tools, etc.) in the case of AM and PM.
- Personnel. The total working days were quantified on the basis of MOTTLES
activities. The unitary cost was calculated as the hourly rate of a technician effectively
employed in the MOTTLES project for the year 2017 (taxes included according to Italian
regulation).
- Travels. For installation, data collection, and maintenance, field visits by operators
are required (as explained above). Considering the average 400 km of distance between the
remote and the central sites, we calculated the total km travelled by car, and related costs.
Tables 4–6 list the components to calculate the amount of each cost category (materials, personnel, and travels) for AM, PM-OGAWA, and PM-IVL divided into installation,
ordinary maintenance/data collection, and extraordinary maintenance. For AM, costs for
each item were considered as the cost effectively incurred for the installation, maintenance,
and data collection of the MOTTLES sites in 2017.
Table 4. Costs of active monitoring station items listed separately per installation, ordinary maintenance, extraordinary
maintenance, and data collection for deciduous forests (DF) and evergreen Mediterranean forests (EF). fr., frequency.
Item
INSTALLATION
Materials/consumables
Ozone Monitor model 106-L 2bTECH
Ozone Monitor enclosure
Ozone sensor screen
Solar screen
Inlet filter
Data acquisition system
Data logger Campbell CR 300
Protective box
Modem
Support structure + power supply system with photovoltaic
panels +assembly material
Sample inlet and tubing
Battery (100 Ah)
Personnel (hourly rate)
Travels
EXTRAORDINARY MAINTAINANCE
Cost (€)
n. per Site
fr. (Times/Year)
4456
426
101.5
102
4
1
1
1
1
1
0
0
0
0
0
2100
350
300
1
1
1
0
0
0
3197
1
0
359.3
80
31.5
68.4
1
1
28.5
2
0
0
0
0
Environments 2021, 8, 104
11 of 20
Table 4. Cont.
Item
Materials/consumables
Replacement parts (pump, lamp, battery)
Personnel
Travels
ORDINARY MAINTAINANCE
Scrubber
Filter
Personnel
Travels
DATA COLLECTION
Cost for data transmission via GPRS (SIM card)
Cost (€)
n. per Site
fr. (Times/Year)
1046.15
31.5
68.4
1
28.5
2
0.2
0.2
0.2
58.7
4
31.5
68.4
1
1
28.5
2
1
6
4
4
50
1
1
Table 5. Costs of passive monitoring station items with OGAWA sensors, listed separately per installation, ordinary
maintenance, and data collection for deciduous forests (DF) and evergreen Mediterranean forests (EF). Extraordinary
maintenance is not required.
Item
Cost (€)
n. per Site
fr.
INSTALLATION
Materials/consumables
Passive sampler OGAWA with airtight storage vial
(including components) + pad
109.6
2
0
4
31.5
68.4
1
14.25
2
0
0
0
31.5
31.5
68.4
68.4
820.8
1
1
2
2
12 t/y
18 t/y
12 t/y
18 t/y
40.2
40.2
2
2
12 t/y
18 t/y
Support for sensor: steel bar
Personnel
Travels
ORDINARY MAINTAINANCE
Personnel per DF
Personnel per EF
Travels per DF
Travels per EF
121 travels (12/y)
DATA COLLECTION
Analyses and filters per DF
Analyses and filters per EF
Table 6. Total cost of passive monitoring station items with IVL sensors, listed separately per installation, ordinary
maintenance, and data collection for deciduous forests (DF) and evergreen Mediterranean forests (EF). Extraordinary
maintenance is not required.
Item
Cost (€)
n. per Site
fr.
INSTALLATION
Materials/consumables
Passive sampler IVL with airtight storage vial
(including components) + pad
55
2
0
4
31.5
68.4
1
14.25
2
0
0
0
31.5
31.5
68.4
68.4
820.8
1
1
2
2
12 t/y
18 t/y
12 t/y
18 t/y
55
55
2
2
12 t/y
18 t/y
Support for sensor: steel bar
Personnel
Travels
ORDINARY MAINTAINANCE
Personnel per DF
Personnel per EF
Travels per DF
Travels per EF
121 travels (12/y)
DATA COLLECTION
Analyses and filters per DF
Analyses and filters per EF
Environments 2021, 8, 104
12 of 20
To simulate the three time scenarios, we calculated the monetary present value, PV0 ,
at the first year of installation (2017) of all the installations and future maintenance costs,
assuming an installation duration, T, of 5, 10, or 20 years. The present value formula is
as follows:
T
Ct
(1)
PV0 = ∑
t
t =1 (1 + r )
where Ct is the annual cost at the year t (5, 10, or 20 years from installation), and r is a 5%
discount rate [54]. National governments assume discount rates ranging between 3.5%
and 15%, and 5% is considered a value coherent with average market interest rates and
discounts adopted to calculate the social cost of carbon [55].
2.5. Social Costs
As for the monetary cost, we compared the present value of the social costs of annual
CO2 emissions caused by the management of the two monitoring systems. Emissions
accumulation in the atmosphere would cause loss of productivity in many economic
sectors and health-related impacts. These outcomes can be translated into monetary terms,
and thus adopted to evaluate the costs and benefits of emission reduction control. The SCC
reflects the present value of such future costs caused by an additional ton of CO2 . Therefore,
it represents the monetary value of the future global damages done by emitting one more
ton of carbon today [56]. The literature supplies a strand of SCC values, depending on
climate change projection damage modeling [57]. We used the values developed by the US
EPA (Interagency Working Group on Social Cost of Greenhouse Gases, 2016), since they
are officially utilized by the US Federal Government for cost-benefit analysis [58], while
equivalent guidelines at EU levels are not yet available. EPA provides SCC at different
social discount rates; in fact, sensitivity analysis is fundamental to provide a range of future
options in a context of uncertainty about the future damages of climate change. Thus, we
calculated the total SCC over 5, 10, and 20 years of AM and PM by using SC-CO2 values
provided by EPA at a social discount rate of 5%. These values are reported in EUR 2019.
3. Results
3.1. Environmental Assessment
The environmental profiles of PM and AM were different depending on the time
window considered as the monitoring duration (Table 7). In the case of Mediterranean
evergreen forests (year-long), AM was better than PM, except for acidification (AP) and
human toxicity potential (HTP) over 5 years and AP over 10 years. In the case of deciduous
forests (April–September growing season), PM had better results than PM in EF, being the
best monitoring strategy in terms of AP and HTP in all time frames, as well as eutrophication potential (EP) over 5 years. In short, AM was generally a better option than PM in
terms of emissions for longer monitoring periods, i.e., 10–20 years and year-long growing
season, except for AP and HTP. The main reason behind these results is related to travels.
Indeed, PM requires frequent accesses to the site, which means a high number of travels
to and from the site. AM requires more instruments and materials but less resources in
terms of human presence. For these reasons, the environmental profiles are influenced
mainly by travels for PM and by materials for AM, and this is the reason why AM is better
in the longer periods while PM is better in the shorter periods. Moreover, travels are the
environmental hotspot for PM in all the impact categories, being the cause of more than
90% of total emissions in all the cases. For this reason, in some impact categories heavily
influenced by the emissions of car engines, such as global warming potential (GWP), the
environmental profile of AM was always better than that of PM, with high differences in
terms of total emissions. A detailed analysis of each impact category is found below.
Environments 2021, 8, 104
13 of 20
Table 7. Results of LCIA for a monitoring site at a distance of 400 km. PM, Passive monitoring; AM, active monitoring; DF,
deciduous forest; EF, evergreen Mediterranean forest. Impact categories: AP= Acidification Potential; EP= Eutrophication
Potential; GWP= Global Warming Potential; HTP= Human Toxicity Potential; ODP= Ozone layer Depletion Potential;
POCP= Petrochemical Ozone Creation Potential. For each time scenario (5, 10 and 20 years from installation), total emissions
are reported in different colours that highlight the best (green), worst (red) and intermediate (orange).
AP
[kg SO2-Equiv.]
EP
[kg
Phosphate-Equiv.]
GWP 100 years
[kg CO2-Equiv.]
HTP inf.
[kg DCB-Equiv.]
ODP
[kg R11-Equiv.]
POCP
[kg Ethene-Equiv.]
Travel
Material
Total
Travel
Material
Total
Travel
Material
Total
Travel
Material
Total
Travel
Material
Total
Travel
Material
Total
PM-DF
5 years
PM-EF
AM
PM-DF
10 years
PM-EF
AM
PM-DF
20 years
PM-EF
AM
25.1
0.8
25.9
6.9
0.5
7.4
10,886
131
11,018
3163
108
3271
1.91E-03
2.04E-05
1.93E-03
5.3
0.1
5.4
37.5
1.1
38.6
10.3
0.7
11.0
16,240
193
16,433
4718
140
4858
2.85E-03
3.04E-05
2.88E-03
8.0
0.1
8.1
9.1
45.7
54.8
2.5
7.0
9.5
3926
1094
5020
1141
4322
5463
6.89E-04
1.20E-04
8.09E-04
1.9
2.1
4.0
49.8
1.5
51.3
13.7
0.9
14.6
21,594
255
21,849
6273
171
6445
3.79E-03
4.04E-05
3.83E-03
10.6
0.1
10.7
74.5
2.2
76.7
20.5
1.4
21.9
32,302
379
32,681
9384
234
9619
5.67E-03
6.03E-05
5.73E-03
15.9
0.2
16.1
17.7
69.0
86.7
4.9
8.9
13.8
7674
1344
9018
2229
5960
8189
1.35E-03
1.43E-04
1.49E-03
3.8
3.1
6.9
99.2
2.9
102.1
27.3
1.9
29.1
43,009
503
43,513
12,495
298
12,792
7.55E-03
8.03E-05
7.63E-03
21.1
0.3
21.4
148.6
4.3
152.9
40.8
2.8
43.6
64,425
751
65,176
18,716
424
19,140
1.13E-02
1.20E-04
1.14E-02
31.7
0.4
32.1
35.0
115.5
150.5
9.6
12.6
22.3
15,169
1844
17,013
4407
9235
13,642
2.66E-03
1.90E-04
2.85E-03
7.5
5.1
12.6
3.1.1. Acidification Potential (AP)
Total emission of sulfuric acid equivalents was higher for AM than for PM in most of
the examined cases, and PM-DF was always better than PM-EF. In fact, AM was better than
PM only in the 20-year scenario when compared with PM-EF. In all cases, PM-DF was better
than AM, especially in the 5- and 10-year scenarios. The shorter the period of monitoring,
the higher the differences between AM and PM. In the 5-year monitoring, AM was 111%
higher than PM-DF and 42% higher than PM-EF, while the difference decreased over 10 and
20 years; in this last case, the value of SO2 eq for AM was 47% higher than PM-DF, and
about 1% lower than PM-EF. Materials and travels had different importance in determining
the emissions of the two types of monitoring; travels were the cause of more than 97% of
total SO2 eq emissions in all cases, while, in AM, materials were the environmental hotspot
for AP, being at least 76.8% of total emissions (up to 83.5% in 5-year monitoring).
3.1.2. Eutrophication Potential (EP)
The emission of Peq was the highest in PM-EF. In the passive systems, EP was mainly
related to travels (always over 93% of total emissions), while, in the AM, travels never
influenced more than 44% of the total emissions. In terms of total emissions, PM-DF
were the best for 5-years monitoring, while AM was the best for 10 and 20 years. In fact,
emissions of phosphate equivalents were 29% higher for AM than PM-DF in 5 years, while
PM-DF was 6% and 31% higher than AM after 10 and 20 years of monitoring, respectively.
3.1.3. Global Warming Potential (GWP)
This impact category was measured as the total amount of CO2 eq. In all examined
cases, AM showed markedly lower emissions than PM, in both DF and EF. In the 5-year
monitoring, PM in DF showed double emission than that of AM, and PM-EF CO2 eq
emitted was three times the CO2 eq emitted in AM. The difference in emissions increased
over longer monitoring; in fact, after 20 years, PM in deciduous forest and in evergreen
forests was 156% and 283% higher than AM, respectively. Travels had a key role in GWP,
contributing up to 98.8% of CO2 eq emissions in PM, and from 78.2% (5 years) to 89.2%
(20 years) in AM.
Environments 2021, 8, 104
14 of 20
3.1.4. Human Toxicity Potential (HTP)
HTP was expressed as 1,4-dichlorobenzene equivalents/kg emission (DCBeq). As for
AP, in DF, PM showed the best environmental profile in comparison with AM, which was
67%, 27%, and 7% higher after 5, 10, and 20 years of monitoring, respectively. Regarding
EF, PM was better (11% lower emissions of DCBeq) than AM in the 5-year time window,
while it is the worst in 10 and 20 years of monitoring duration (17.5% and 40% higher).
While travels related with data collection were the environmental hotspot in PM, in AM,
the highest contribution to HTP was related to the materials used in the monitoring station
(79% to 68% of total emissions from the 5-year to 20-year time window).
3.1.5. Ozone Layer Depletion Potential (ODP)
For this impact category, the potential impact was reported as trichlorofluoromethane
equivalent emissions (kg CFC-11eq), with higher emissions for PM than for AM. Overall,
CFC emissions were low, with the highest values occurring in the case of PM in every
time window and both forest types, and the highest amount of emissions concentrated on
travels (from 85% in AM to 98% in PM).
3.1.6. Photochemical Ozone Creation Potential (POCP)
This category evaluated the emission of ethene equivalent (C2 H4 eq) as reference for
potential photochemical oxidant creation. The emissions were lower in AM than in PM for
both forest types. The total impact of AM was from 34% (5-years) to 70% lower (20-years)
than PM in DF. In EF, the difference was higher and emissions of C2 H4 eq in PM were 100%,
133%, and 155% higher than in AM for 5-, 10-, and 20-year monitoring, respectively.
3.2. Economic Costs
No marked differences were observed between Ogawa and IVL passive sensors
(Figure 4). AM resulted in the most convenient system, as the costs at 5 years from
installation were 33% and 37% lower than PM in DF and 56% and 58% lower than PM in EF.
At 10 and 20 years, the monetary savings of AM were even higher, as the costs of the two
passive systems were, on average, 49% and 66% lower than PM in DF and EF at 10 years,
and 55% and 70% lower at 20 years for OG and IVL, respectively. The cost category material
varied with the type of monitoring, while travel and personnel were constant costs for both
OGAWA and IVL passive systems within the same type of forest. The cost category that
mainly affected all monitoring costs was personnel, representing between 60.2% and 75.6%
of the total cost. The personnel had the lowest cost in AM (EUR 7749, EUR 13,820, and
EUR 22,305 in 5, 10, and 20 years, respectively) and the highest cost in the two PM systems
for evergreen forests (EUR 34,870, EUR 62,192, and EUR 100,372 in 5, 10, and 20 years,
respectively). The amount of personnel cost was followed by material (13.5–35.3%) and
travels (4.5–11.5%). The lowest cost for the material was calculated for OG-DF: EUR 3898,
EUR 7561, and EUR 12,135 at 5, 10, and 20 years, respectively. The highest material cost
was calculated for AM in each time scenario, with the highest percentage over the shortest
5-year period (35.3%). Regarding the travel category, the highest costs were attributed to
both types of PM for evergreen forests: EUR 2665, EUR 4753, and EUR 7671 in the three
time windows, respectively.
3.3. Social Costs
The SCC of PM for sites at a distance of 400 km from the control station result was
always higher than the SCC of AM (Figure S1) (see Supplementary Materials). Within PM,
the SCC related with the monitoring of EF was EUR 253, 480, and 863 after 5, 10, and 20
years, respectively, i.e., ca. 50% higher than in DF, regardless of the time window. The
SCC of AM was EUR 78, 134, and 228, i.e., 60, 58, and 54% lower than PM-DF, and 74, 72,
and 69% lower than PM-EF after 5, 10, and 20 years, respectively. In total, AM showed
markedly lower CO2 equivalent emissions than PM, both in DF and EF, leading to a saving
of EUR 1563 in DF and EUR 2982 in EF after 5 years per the 400-km site. Those savings
Environments 2021, 8, 104
15 of 20
increased up to EUR 3185 and EUR 5890 in DF and EF after 10 years, and to EUR 5920 and
EUR 10,791 in DF and EF after 20 years.
Figure 4. Monetary costs (EUR) of the monitoring systems, i.e., passive monitoring with either IVL
(IVL) or Ogawa (OG) sensors, and active monitoring (AM) for deciduous (DF) and evergreen (EF)
forests over three time windows, i.e., 5, 10, and 20 years of monitoring.
4. Discussion
The debate between passive and active monitoring has been a pressing dilemma in
assessing air pollution at remote forest sites [26,27,29–31]. For the first time, we present
observational evidence that the sustainability of active monitoring is now better than that
of passive sensors, as the environmental, economic, and social costs are usually lower in
AM than in PM. In detail, we found a greater environmental sustainability of the active
system after 10 years from installation, while social and economic impacts of the active
system were lower, even after 5 years. The type of forest determined important differences,
as the costs of PM for Mediterranean evergreen forests were always higher than those for
deciduous forests; hence, in this forest type, the convenience of the active system is realized
earlier. These results were seriously affected by the high costs of personnel in Italy, where
our case studies were applied. Our approach warrants further studies in different countries
across a range of forest and economic conditions.
4.1. Environmental Sustainability
The results of the environmental impact assessment highlighted an important role of
personnel transport on the total amount of emissions. This is a common result obtained in
similar studies applied to different sectors, when the analyzed system has low emissions;
in these cases, secondary processes become important contributors to the total amount of
emissions [59], while, in many other cases, the role of personnel transport is negligible
in comparison with the overall emissions [60]. As travels were the major environmental
hotspot in our analysis, the influence of travel distance was further investigated by two
short-range (30 km) and long-range (750 km) scenarios to quantify the role of distance in
terms of total emissions. The LCIA was repeated for both scenarios, considering the same
LCI as the case study, changing only the travel distance. PM was almost totally influenced
by travel distance, from 93% in EP to 99% in ODP, while AM had a strong component of
other inputs (materials) that attenuated the effects of travels on total emissions (Figure S2).
In fact, in the 30-km scenario PM had a better environmental profile than AM. On the
contrary, in the 750-km scenario, AM was often better than PM. In detail, in the shortrange scenario, PM (in both EF and DF) had lower emissions than AM in acidification,
Environments 2021, 8, 104
16 of 20
eutrophication, human toxicity, and O3 creation potential (POCP) in all time frames, while
in ODP AM was always better than PM-EF and better than PM-DF in the 10 and 20 year
time frames; regarding total emissions of CO2 eq (GWP), AM was always better than
PM-EF, but not PM-DF, in the 5- and 10-year scenarios. On the other hand, in the longrange scenario AM had a better environmental profile than PM for eutrophication, global
warming, O3 layer depletion, and O3 creation potential in all time windows and forest
types; regarding acidification potential, PM was better than AM only in the 5- and 10-year
scenarios in deciduous forests.
4.2. Economic Sustainability
When LCA is combined with an economic analysis, results are more policy relevant, as
supported by previous studies [40,61,62]. Similar to other studies conducted in forests, e.g.,
woody product supply [63,64], the cost category that mainly affected costs in the present
study was personnel, suggesting possible different results for monitoring comparisons
conducted in countries with different labor costs. As PM requires more frequent travels
and, thus, also higher costs of personnel, the monetary cost was always lower for AM.
Despite the higher installation costs, AM led to savings ranging from a minimum of EUR
9650 in 5 years in the case of DF up to EUR 94,796 in the case of EF after 20 years. In
particular, in the short term, the personnel costs for the collection of PM data exceeded the
high cost of AM installation, confirming the convenience of AM. The cost of AM material
was lower than that of IVL-EF in the medium term, and of IVL-DF and OG-EF in the long
term, suggesting the possibility for managers to replace the passive with the active system
in both forest types. The economic convenience of the active system was confirmed in both
the long- and short-distance scenarios (Figure S3), despite the large difference among those
scenarios in terms of travel contribution to the total costs (from a maximum of 1% in the
30-km scenario to a maximum of 19.5% in the 750-km scenario).
About the passive systems, OGAWA was more convenient than IVL in every case.
Such results were related to a lower cost of data analysis; hence, results could change if
analysis would be conducted within the research center without extra costs.
4.3. Social Sustainability
Very few studies [45,65] calculated the social cost of the CO2 eq. assessed by the LCA
analysis, and it is also probable that none of them included a monetary cost analysis. Then,
the present study represents a first experience in the assessment of the three pillars of
sustainability (environmental, economic, and social) in the ozone monitoring of forests.
SCC confirmed AM as the most convenient monitoring system, also, in terms of social
costs in both forest types, even after 5 years, with a higher convenience at 10 and 20 years.
This is not surprising, since SCC is directly related with the global warming potential
assessed by LCA.
By applying the distance scenario also to the social impacts, we found that the SCC
determined by AM was always lower than PM for the 750-km scenario (savings from
EUR 3158 up to EUR 20,483), since travels represented the cause of more than 90% of
total emission (Figure S1); in contrast, in the 30-km scenario, PM showed an SCC lower
than AM for DF in the medium term (10 years), and for DF and EF in the short term
(5 years). A further sensitivity analysis, considering different discount rates, was realized
in order to estimate the maximum social cost of both methods. In particular, according to
US EPA, we also considered the high-impact scenario, which considers lower-probability,
higher-impact outcomes of climate change that would be particularly harmful to society
and, thus, relevant to the public and policymakers (calculated as the 95th percentile of
the frequency distribution of SC-CO2 estimates based on a 3% discount rate). Under this
scenario, after 20 years, the SCC of AM reached EUR 2201, while PM was EUR 5544 in DF
and EUR 8301 in EF (Figure S1).
Environments 2021, 8, 104
17 of 20
5. Conclusions
This LCA study estimated the three types of cost associated to active or passive ozone
monitoring in forest plots. These innovative results support active O3 monitoring rather
than passive monitoring. Indeed, despite the high installation costs that make active
monitoring still little used, the costs of active monitoring are already compensated after
5 years of monitoring due to the large incidence of personnel costs on sample collection in
passive monitoring. The advantage of active monitoring is greater in the longer monitoring
windows, e.g., for the evergreen Mediterranean forests requiring all-year-long monitoring.
The advantage of AM compared to PM is driven by the lower number of trips from the
research center to forest sites. In particular, the reduced global warming potential of active
monitoring leads to social benefits quantified in terms of social cost of carbon. These
results indicated an advantage of active monitoring from an environmental, economic,
and social point of view. In addition, the active system supplies reliable hourly data that
are suitable for stomatal O3 flux estimation, supporting policymakers in assessing ozone
impacts on forests.
Supplementary Materials: The following are available online at https://www.mdpi.com/article/10
.3390/environments8100104/s1, Figure S1: Environmental impacts of the two monitoring methods
passive (PM) and active (AM) in the three time frames (5, 10, 20 years) at the two scenarios (a-30 km
and b-750 Km) on the six impact categories: acidification potential (AP), Eutrophication Potential
(EP), Global Warming Potential (GWP), Human Toxicity Potential (HTP), Ozone Layer Depletion
Potential (ODP), Photochemical Ozone Creation Potential (POCP). Results for PM is separated into
the two forest types deciduous (PM-DF) and evergreen (PM-EF). Bar colours are referred with the
input category (white = material; black); Figure S2: Monetary costs (€) of the monitoring systems,
i.e., passive monitoring with either IVL (IVL) or Ogawa (OG) sensors, and active monitoring (AM)
for deciduous (DF) and evergreen (EF) forests over 5, 10 and 20 years of activity at the two distance
scenarios, i.e., 30 km and 750 km from the forest site to the control base; Figure S3: Social cost of
carbon in active (AM) and passive monitoring (PM), the latter is divided into deciduous forest (DF)
and evergreen Mediterranean forest (EF), when the monitoring site is 400, 30 or 750 km distant from
the control base, at 5, 10 and 20 years from installation, and with different discount rates (5, 3, 2.5 and
HI, high impact, e.g. 95th percentile at 3%).
Author Contributions: Conceptualization, E.C., A.D.M., A.L. and E.P.; methodology, A.M., E.P., E.C.
and A.L.; software, A.L. and I.P.; investigation, E.C., L.D.-R., S.F., Y.H., S.L., D.P., G.P., P.S. and I.P.;
resources, E.P., O.B. and S.F.; data curation, E.C., S.L., A.D.M., P.S. and G.P.; writing—original draft
preparation, E.C. and A.L.; writing—review and editing, E.P., E.M. and A.D.M.; supervision, E.P. and
O.B.; project administration, E.P.; funding acquisition, E.P., O.B. and S.F. All authors have read and
agreed to the published version of the manuscript.
Funding: This research was funded by European Community, grant number LIFE15 ENV/IT/000183
and the NEC Italia project co-ordinated by CUFA.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
References
1.
2.
3.
4.
Moore, J.E.; Mascarenhas, A.; Bain, J.; Straus, S.E. Developing a comprehensive definition of sustainability. Implement. Sci. 2017,
12, 110. [CrossRef] [PubMed]
Daily, G.C.; Polasky, S.; Goldstein, J.; Kareiva, P.M.; Mooney, H.A.; Pejchar, L.; Ricketts, T.H.; Salzman, J.; Shallenberger, R.
Ecosystem services in decision making: Time to deliver. Front. Ecol. Environ. 2009, 7, 21–28. [CrossRef]
Endris, K.; Marco, T.; Sergio, T.; Gokan, M. Integration of sustainability in NPD process: Italian Experiences. In Proceedings of the
PLM 2011—The IFIP WG51—8th International Conference on Product Lifecycle Management, Eindhoven, The Netherlands,
11–13 July 2011.
Rebitzer, G.; Ekvall, T.; Frischknecht, R.; Hunkeler, D.; Norris, G.; Rydberg, T.; Schmidt, W.P.; Suh, S.; Weidema, B.P.; Pennington,
D.W. Life cycle assessment: Part 1: Framework, goal and scope definition, inventory analysis, and applications. Environ. Int. 2004,
30, 701–720. [CrossRef] [PubMed]
Environments 2021, 8, 104
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
18 of 20
Zhang, Y.I.; Singh, S.; Bakshi, B.R. Accounting for Ecosystem Services in Life Cycle Assessment, Part I: A Critical Review. Environ.
Sci. Technol. 2010, 44, 2232–2242. [CrossRef] [PubMed]
Pennington, D.W.; Norris, G.; Hoagland, T.; Bare, J.C. Environmental comparison metrics for life cycle impact assessment and
process design. Environ. Prog. 2000, 19, 83–91. [CrossRef]
Pennington, D.; Potting, J.; Finnveden, G.; Lindeijer, E.; Jolliet, O.; Rydberg, T.; Rebitzer, G. Life cycle assessment Part 2: Current
impact assessment practice. Environ. Int. 2004, 30, 721–739. [CrossRef]
Wenzel, H.; Hauschild, M.; Alting, L. (Eds.) Environmental Assessment of Products; Kluwer Academic Publisher: Dordrecht, The
Netherlands, 1997; Volume 1.
Baumann, H.; Tillman, A.-M. The Hitch Hiker’s Guide to LCA.; The Authors and Student Literature: Lund, Sweden, 2004.
Klein, D.; Wolf, C.; Schulz, C.; Weber-Blaschke, G. 20 years of life cycle assessment (LCA) in the forestry sector: State of the art
and a methodical proposal for the LCA of forest production. Int. J. Life Cycle Assess. 2015, 20, 556–575. [CrossRef]
Laschi, A.; Marchi, E.; González-García, S. Environmental performance of wood pellets’ production through life cycle analysis.
Energy 2016, 103, 469–480. [CrossRef]
McLaughlin, S.; Percy, K. Forest Health in North America: Some perspectives on Actual and Potential Roles of Climate and Air
Pollution. Water Air Soil Pollut. 1999, 116, 151–197. [CrossRef]
Schaub, M.; Calatayud, V.; Ferretti, M.; Brunialti, G.; Lövblad, G.; Krause, G.; Sanz, M.J. Part XV: Monitoring of Air Quality. In
Manual on Methods and Criteria for Harmonized Sampling, Assessment, Monitoring and Analysis of the Effects of Air Pollution on Forests;
UNECE ICP Forests Programme Co-ordinating Centre, Ed.; Thünen Institute of Forest Ecosystems: Eberswalde, Germany, 2016.
Convention on Long-range Transboundary Air Pollution. Mapping Critical Levels for Vegetation, Chapter III of Manual on
Methodologies and Criteria for Modelling and Mapping Critical Loads and Levels and Air Pollution Effects, Risks and Trends.
Available online: http://icpmapping.org/Publications_CLRTAP (accessed on 16 July 2018).
Totsuka, T.; Sase, H.; Shimizu, H. Major activities of acid deposition monitoring network in East Asia (EANET) and related
studies. In Plant Responses to Air Pollution and Global Change; Springer: Tokyo, Japan, 2005; pp. 251–259. [CrossRef]
Braun, S.; Schindler, C.; Rihm, B. Growth losses in Swiss forests caused by ozone: Epidemiological data analysis of stem increment
of Fagus sylvatica L. and Picea abies Karst. Environ. Pollut. 2014, 192, 129–138. [CrossRef] [PubMed]
De Marco, A.; Screpanti, A.; Paoletti, E. Geostatistics as a validation tool for setting ozone standards for durum wheat. Environ.
Pollut. 2010, 158, 536–542. [CrossRef] [PubMed]
Paoletti, E. Impact of ozone on Mediterranean forests: A review. Environ. Pollut. 2006, 144, 463–474. [CrossRef]
Mills, G.; Sharps, K.; Simpson, D.; Pleijel, H.; Broberg, M.; Uddling, J.; Jaramillo, F.; Davies, W.J.; Dentener, F.; Van den Berg,
M.; et al. Ozone pollution will com-promise efforts to increase global wheat production. Glob. Chang. Biol. 2018, 24, 3560–3574.
[CrossRef] [PubMed]
Agathokleous, E.; Feng, Z.; Oksanen, E.; Sicard, P.; Wang, P.; Saitanis, C.J.; Araminiene, V.; Blande, J.D.; Hayes, F.; Calatayud, V.;
et al. Ozone affects plant, insect and soil microbial communities: A threat to terrestrial ecosystems and biodiversity. Sci. Adv. (in
press). 2020. [CrossRef]
Krupa, S.V.; Manning, W.J. Atmospheric ozone: Formation and effects on vegetation. Environ. Pollut. 1988, 50, 101–137. [CrossRef]
Sicard, P.; Augustaitis, A.; Belyazid, S.; Calfapietra, C.; de Marco, A.; Fenn, M.; Bytnerowicz, A.; Grulke, N.; He, S.; Matyssek, R.;
et al. Global topics and novel approaches in the study of air pollution, climate change and forest ecosystems. Environ. Pollut.
2016, 213, 977–987. [CrossRef]
Aw, J.; Kleeman, M.J. Evaluating the first-order effect of intra annual temperature variability on urban air pollution. J. Geophys.
Res. Atmos. 2003, 108, D12. [CrossRef]
Lefohn, A.S.; Malley, C.S.; Smith, L.; Wells, B.; Hazucha, M.; Simon, H.; Naik, V.; Mills, G.; Schultz, M.G.; Paoletti, E.; et al.
Tropospheric ozone assessment report: Global ozone metrics for climate change, human health, and crop/ecosystem research.
Elem. Sci. Anthol. 2018, 6, 28. [CrossRef]
Anav, A.; De Marco, A.; Proietti, C.; Alessandri, A.; Cionni, I.; Dell’Aquila, A.; Friedlingstein, P.; Khvorostyanov, D.; Menut, L.;
Paoletti, E.; et al. Comparing concentration-based (AOT40) and stomatal uptake (PODY) metrics for ozone risk assessment to
European forests. Glob. Chang. Biol. 2016, 22, 1608. [CrossRef] [PubMed]
Bytnerowicz, A.; Godzik, B.; Fraczek,
˛
W.; Grodzińska, K.; Krywult, M.; Badea, O.; Barančok, P.; Blum, O.; Černy, M.; Godzik, S.;
et al. Distribution of ozone and other air pollutants in forests of the Carpathian Mountains in central Europe. Environ. Pollut.
2001, 116, 3–25. [CrossRef]
Hůnová, I.; Livorová, H.; Ostatnická, J. Potential ambient ozone impact on ecosystems in the Czech Republic as indicated by
exposure index AOT40. Ecol. Indic. 2003, 3, 35–47. [CrossRef]
Calatayud, V.; Schaub, M. Methods for Measuring Gaseous air Pollutants in Forests. Dev. Environ. Sci. 2013, 12, 375–384.
[CrossRef]
Paoletti, E.; Alivernini, A.; Anav, A.; Badea, O.; Carrari, E.; Chivulescu, S.; Conte, A.; Ciriani, M.; Dalstein-Richier, L.; De Marco,
A.; et al. Toward stomatal-flux based forest protection against ozone: The MOTTLES approach. Sci. Total. Environ. 2019, 691,
516–527. [CrossRef] [PubMed]
Krupa, S.; Legge, A. Passive sampling of ambient, gaseous air pollutants: An assessment from an ecological perspective. Environ.
Pollut. 2000, 107, 31–45. [CrossRef]
Environments 2021, 8, 104
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
19 of 20
Tuovinen, J.-P. Assessing vegetation exposure to ozone: Is it possible to estimate AOT40 by passive sampling? Environ. Pollut.
2002, 119, 203–214. [CrossRef]
Cox, R.M. The use of passive sampling to monitor forest exposure to O3, NO2 and SO2: A review and some case studies. Environ.
Pollut. 2003, 126, 301–311. [CrossRef]
Krupa, S.; Nosal, M.; Peterson, D.L. Use of passive ambient ozone (O3) samplers in vegetation effects assessment. Environ. Pollut.
2001, 112, 303–309. [CrossRef]
Krupa, S.; Nosal, M.; Ferdinand, J.; Stevenson, R.; Skelly, J. A multi-variate statistical model integrating passive sampler and
meteorology data to predict the frequency distributions of hourly ambient ozone (O3) concentrations. Environ. Pollut. 2003, 124,
173–178. [CrossRef]
Loibl, W.; Winiwarter, W.; Kopsca, A.; Zufger, J.; Baumann, R. Estimating the spatial distribution of ozone concentrations in
complex terrain. Atmos. Environ. 1994, 28, 2557–2566.
Mazzali, C.; Angelino, E.; Gerosa, G.; Ballarin-Denti, A. Ozone Risk Assessment and Mapping in the Alps Based on Data from
Passive Samplers. Sci. World J. 2002, 2, 1023–1035. [CrossRef]
Loibl, W.; Bolhàr-Nordenkampf, H.R.; Herman, F.; Smidt, S. Modelling critical levels of ozone for the forested area of Austria.
Modifications of the AOT40 concept. Environ. Sci. Pollut. Res. 2004, 11, 171–180. [CrossRef]
De Marco, A.; Vitale, M.; Kiliç, U.; Serengil, Y.; Paoletti, E. New functions for estimating AOT40 from ozone passive sampling.
Atmospheric Environ. 2014, 95, 82–88. [CrossRef]
Calatayud, V.; Diéguez, J.J.; Sicard, P.; Schaub, M.; De Marco, A. Testing approaches for calculating stomatal ozone fluxes from
passive samplers. Sci. Total. Environ. 2016, 572, 56–67. [CrossRef]
Norris, G.A. Integrating life cycle cost analysis and LCA. Int. J. Life Cycle Assess. 2001, 6, 118–120. [CrossRef]
Caughlan, L.; Oakley, K.L. Cost considerations for long-term ecological monitoring. Ecol. Indic. 2001, 1, 123–134. [CrossRef]
Manning, W.J. Detecting plant effects is necessary to give biological significance to ambient ozone monitoring data and predictive
ozone standards. Environ. Pollut. 2003, 126, 375–379. [CrossRef]
Nordhaus, W.D. Revisiting the social cost of carbon. Proc. Natl. Acad. Sci. USA 2017, 114, 1518–1523. [CrossRef] [PubMed]
Tol, S.J.R. The marginal damage costs of carbon dioxide emissions: An assessment of the uncertainties. Energy Pol. 2005, 33,
2064–2074. [CrossRef]
Solinas, S.; Tiloca, M.T.; Deligios, P.A.; Cossu, M.; Ledda, L. Carbon footprints and social carbon cost assessments in a perennial
energy crop system: A comparison of fertilizer management practices in a Mediterranean area. Agric. Syst. 2021, 186, 102989.
[CrossRef]
Weidema, B.P. The Integration of Economic and Social Aspects in Life Cycle Impact Assessment. Int. J. Life Cycle Assess. 2006, 11,
89–96. [CrossRef]
Tavoni, M.; van Vuuren, D.P. 2015 Regional Carbon Budgets: Do They Matter for Climate Policy? Available online: https:
//ssrn.com/abstract=2637298 (accessed on 29 July 2015).
Carmichael, G.R.; Ferm, M.; Thongboonchoo, N.; Woo, J.-H.; Chan, L.; Murano, K.; Viet, P.H.; Mossberg, C.; Bala, R.; Boonjawat, J.;
et al. Measurements of sulfur dioxide, ozone and ammonia concentrations in Asia, Africa, and South America using passive
samplers. Atmos. Environ. 2003, 37, 1293–1308. [CrossRef]
Koutrakis, P.; Wolfson, J.M.; Bunyaviroch, A.; Froehlich, S.E.; Hirano, K.; Mulik, J.D. Measurement of ambient ozone using a
nitrite-coated filter. Anal. Chem. 1993, 65, 209–214. [CrossRef]
Sanz, M.; Calatayud, V.; Sánchez-Peña, G. Measures of ozone concentrations using passive sampling in forests of South Western
Europe. Environ. Pollut. 2007, 145, 620–628. [CrossRef] [PubMed]
Ogawa. Protocol for Ozone Measurement Using the Ozone Passive Sampler Badge. Available online: https://ogawausa.com/
(accessed on 16 August 2021).
Spicer, C.W.; Joseph, D.W.; Ollison, W.M. A Re-Examination of Ambient Air Ozone Monitor Interferences. J. Air Waste Manag.
Assoc. 2010, 60, 1353–1364. [CrossRef]
Manning, W.; Krupa, S.; Bergweiler, C.; Nelson, K. Ambient ozone (O3) in three Class I wilderness areas in the northeastern USA:
Measurements with Ogawa passive samplers. Environ. Pollut. 1996, 91, 399–403. [CrossRef]
Percoco, M. A social discount rate for Italy. Appl. Econ. Lett. 2007, 15, 73–77. [CrossRef]
Emmerling, J.; Drouet, L.; Van Der Wijst, K.-I.; Van Vuuren, D.; Bosetti, V.; Tavoni, M. The role of the discount rate for emission
pathways and negative emissions. Environ. Res. Lett. 2019, 14, 104008. [CrossRef]
Pearce, D. The Social Cost of Carbon and its Policy Implications. Oxf. Rev. Econ. Policy 2003, 19, 362–384. [CrossRef]
Tol, R.S. The social cost of carbon. Annu. Rev. Resour. Econ. 2011, 3, 419–443. [CrossRef]
Metcalf, G.E.; Stock, J.H. Integrated Assessment Models and the Social Cost of Carbon: A Review and Assessment of U.S.
Experience. Rev. Environ. Econ. Policy 2017, 11, 80–99. [CrossRef]
Di Maria, F.; Sisani, F. A life cycle assessment of conventional technologies for landfill leachate treatment. Environ. Technol. Innov.
2017, 8, 411–422. [CrossRef]
Hammervold, J.; Reenaas, M.; Brattebø, H. Environmental Life Cycle Assessment of Bridges. J. Bridge Eng. 2013, 18, 153–161.
[CrossRef]
Atia, N.G.; Bassily, M.A.; Elamer, A.A. Do life-cycle costing and assessment integration support decision-making towards
sus-tainable development? J. Clean. Prod. 2020, 267, 122056. [CrossRef]
Environments 2021, 8, 104
62.
63.
64.
65.
20 of 20
Petersen, A.K.; Solberg, B. Environmental and economic impacts of substitution between wood products and alternative materials:
A review of micro-level analyses from Norway and Sweden. For. Pol. Econ. 2005, 7, 249–259. [CrossRef]
Mani, S.; Sokhansanj, S.; Bi, X.; Turhollow, A. Economics of producing fuel pellets from biomass. Appl. Eng. Agric. 2006, 22,
421–426. [CrossRef]
Thek, G.; Obernberger, I. Wood pellet production costs under Austrian and in comparison to Swedish framework conditions.
Biomass- Bioenergy 2004, 27, 671–693. [CrossRef]
Cao, V.; Margni, M.; Favis, B.D.; Deschênes, L. Aggregated indicator to assess land use impacts in life cycle assessment (LCA)
based on the economic value of ecosystem services. J. Clean. Prod. 2015, 94, 56–66. [CrossRef]