AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-3313-2017Spatial estimation of air PM2.5 emissions using activity data, local
emission factors and land cover derived from satellite imageryGibeHezron P.CayetanoMylene G.mcayetano@iesm.upd.edu.phInstitute of Environmental Science and Meteorology, University of the
Philippines, Diliman, 1101 Quezon City, PhilippinesInternational Environmental Research Institute, Gwangju Institute of
Science and Technology, Cheomdan-gwagiro, Buk-gu, 500-712 Gwangju, South
KoreaMylene G. Cayetano (mcayetano@iesm.upd.edu.ph)11September20171093313332322January201724March201717July201720July2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/10/3313/2017/amt-10-3313-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/3313/2017/amt-10-3313-2017.pdf
Exposure to particulate matter (PM) is a serious environmental
problem in many urban areas on Earth. In the Philippines, most existing
studies and emission inventories have mainly focused on point and mobile
sources, while research involving human exposures to particulate pollutants
is rare. This paper presents a method for estimating the amount of fine
particulate (PM2.5) emissions in a test study site in the city of Cabanatuan,
Nueva Ecija, in the Philippines, by utilizing local emission factors,
regionally procured data, and land cover/land use (activity data) interpreted
from satellite imagery. Geographic information system (GIS) software was used
to map the estimated emissions in the study area. The present results suggest
that vehicular emissions from motorcycles and tricycles, as well as fuels
used by households (charcoal) and burning of agricultural waste, largely
contribute to PM2.5 emissions in Cabanatuan. Overall, the method
used in this study can be applied in other small urbanizing cities, as long
as on-site specific activity, emission factor, and satellite-imaged land
cover data are available.
Map data (satellite and
street-level imagery) used in this study are copyrighted (2015, 2016) to
Google and data providers: Landsat, Copernicus, ZENRIN, and SKEnergy.
Additional map data copyrighted to OpenStreetMap contributors and available
from https://www.openstreetmap.org.
Introduction
Exposure to air particulate matter, especially fine particles smaller than
2.5 µm in
size (PM2.5), can reduce air quality, affect visibility through smog and
other haze phenomena, and introduce lasting effects on climate on a local and
regional scale. Exposure to pollutants is a risk for many people living in
urban areas, since the level of pollution frequently exceeds WHO guideline
values (Mage et al., 1996). The presence of PM2.5 is linked to increased
morbidity and mortality risk, especially in incidences of various
cardiopulmonary diseases (Chen et al., 2008; Lin et al., 2016; Wu et al.,
2013), birth defects (Goto et al., 2016), and cancer (Cassidy et al., 2007).
PM2.5 pollution is also considered carcinogenic, especially exposure to
the finest fractions (ultrafine particles) (Bocchi et al., 2016). This can be
attributed to particles acting as carriers of mutagenic and genotoxic
compounds (Chen et al., 2016).
Enhancements in PM2.5 are mainly caused by various human activities. A
common source of particles contributing to PM2.5 in urban areas is
related to mobile sources, directly emitted by internal combustion processes
inside vehicles of all types (Andrade et al., 2012; Ahanchian and Biona,
2014; Chen et al., 2016). In most of the reports from Philippine cities,
vehicular emissions reported in inventories use foreign emission factors
(such as the 2007 version of the CORINAIR emission guidebook, EEA, 2007, and
the Compilation of Air Pollutant Emission Factors (AP 42), EFIG, 1995). However, PM2.5
emissions from other activities such as burning of agricultural waste also occur in cities with a mixture of rural and urban land uses (Sarigiannis et
al., 2014; Kim Oanh et al., 2011; Gadde et al., 2009).
At present, air quality monitoring and management are based on PM10 and
total suspended particles as an indicator. Standards for PM2.5
have, however, not been fully developed and implemented in small cities.
Emission inventories in general have likewise not been constructed in many
cities. In addition, previous investigations are rare and limited in time,
which means that temporally resolved long-term air quality monitoring data
are not available.
This study presents a method to estimate PM2.5 by utilizing locally
determined emission factors, satellite imagery, and activity data. The
latter is obtained from interpretation of geographic information system
(GIS) data and by identifying and localizing all sources in the city, taking
into account the type of emission (point, area, mobile) and activities
producing the emissions. This includes factors such as local population,
density of households, number of emission-generating events, and the type
and amount of various fuels used. This, in conjunction with various local
emission factors, will be used to estimate total PM2.5 emissions. A
limitation of this study is that all emission sources are treated as being
area sources, since this is required in the mapping process.
From the resulting maps, the study aims to determine areas of high
concentration of PM2.5, caused by individual and several aerosol
sources. The present method can specifically be used for similar mixtures of
manmade activities present in Philippine cities. This study is specifically
meant to explore this method for use in relatively small regional urban
centers and cities in the Philippines, especially due to these cities being
situated in locations where there is a mixture of rural and urban
activities. Sources corresponding to rural activity include open burning of
agricultural waste and the usage of household cooking fuels such as
charcoal. Sources corresponding to urban activity include vehicular mobile
sources such as tricycles, jeepneys (colloquially known as XLTs), and public utility vehicles (PUVs,
which include buses and vans). Another application for this study is
planning aids for local governments, as the present method can be used in
emission inventories for small cities. The method was developed to be used
with minimal required training and effort by stakeholders in order to
create emission inventories of aerosol sources in the cities.
Materials and methodsStudy area
The test study was conducted in the city of Cabanatuan, Philippines. It is the
former capital and largest city of the province of Nueva Ecija, with a land
area of 190.67 km2 and an estimated population of 296 584 in
2012. On average, the population density is around 1516 persons per square
kilometer. The urban and rural populations each constitute about half of the
total population (CPDO, 2015). A map of Cabanatuan with
a reference to its nearby major cities is shown in Fig. 1.
Map of the Philippines and location of Cabanatuan (with major
cities).
A 2.4 by 4.0 km area including the city center and its nearest
environs was selected as the main study area. The town proper (locally
known as the poblacion) is highlighted in the map of the study area shown in Fig. 2.
Grey lines indicate boundaries of barangays (the smallest administrative division of
a local government, a similar concept to town wards or districts), and the
constituent barangays of the poblacion are marked using thicker grey outlines. The
investigation area includes residential and commercial zones and even
agricultural areas less than 2 km away from a main road. A
commercial zone and the planned main industrial district in Cabanatuan
located south and about 8–10 km southeast of the investigation area,
respectively, are not taken into consideration in the study.
The 2.4×4.0 km study area in Cabanatuan containing
the “city center” (poblacion, highlighted). Base map derived from satellite imagery (Google Earth Pro, 2015, 2016). Additional data from OpenStreetMap (OpenStreetMap contributors, 2016).
Land cover classification using satellite imagery
The investigation area was divided with 24×40 grid cells (100×100 m or 1 ha/0.01 km2 each). For each cell, the type of
manmade activity was interpreted from satellite images taken from Google
Earth software. The classification process is similar to methods of
supervised classification of land cover, as utilized by current local
training activities on emission inventories such as the Clean Air for Smaller
Cities (CASC) project (Yuberk and Cornet, 2013). The image of the surface
feature is compared to a reference area of known land cover. Due to the size
of each cell, the detail of each ground feature can be clearly seen. Detailed
images over the ground, taken by Google Street View (examples are shown in
Fig. 3), were used to verify building types (residential/commercial). Satellite
images were dated 3 March 2016, while ground level (Street View) images were
dated September 2015 (Google Earth Pro, 2015, 2016). Additionally, maps from
OpenStreetMap were also used for identifying special landmarks or as an
additional resource since they occasionally present more updated information
on surface features than Google Earth or Google Street View (OpenStreetMap
contributors, 2016).
Example of reference image used for Google Street View verification
of surface features (Google Earth Pro, 2016).
Land cover/land use map from interpretation of satellite image. Base map derived from satellite imagery (Google Earth Pro, 2015, 2016). Additional data from OpenStreetMap (OpenStreetMap contributors, 2016).
Google Earth images have been used here instead of raw image data from, for
example, the Landsat satellite. The Google Earth images used consist of
post-processed Landsat images from the European Space Agency's (ESA)
Copernicus program. This is because the method developed in this study is
intended to be used by personnel not necessarily familiar with processing of
satellite raw imagery data. The Google Earth images have been processed to
minimize the presence of clouds and corrected for aberrations from the
camera taking the satellite images. These images are not representative of
the most current features on the ground. There is also a slight deviation of
the actual coordinates representing the location of the area due to the
orthographic projection of the satellite image. This is consistent with
geolocation deviations present in most consumer-grade satellite/GPS
products. It is also difficult to get access to the metadata of the original
images. Despite these disadvantages, the Google satellite image product is
useful enough for the uninitiated considering the present purpose. In
addition, other data products such as Google Street View or OpenStreetMap
(community-based initiative) can be used. The usage of supporting documents
such as existing local government land use plans and land cover maps, as
well as actual verification of features at the ground level (ground truth,
i.e., information on surface features in the study area), is necessary
and in this study verified land cover and land use features at the
surface level.
PM2.5 emissions in Cabanatuan depend highly on local activity.
Therefore, each grid cell (100×100 m) within the study area has been
classified with respect to the land cover features, i.e.,
residential/commercial zones, agricultural areas, or other surface
characteristics. Figure 4 shows that residential land use (cells marked as
“Residential (LPG)”; households using liquefied petroleum gas as a fuel)
is spread widely, although with noticeable commercial districts and open
fields (not settled or occupied) located within this area. Two large
agricultural areas are found in the northwest and east, occupied by small
households likely using biomass-based fuels like charcoal (cells marked as
“Residential (Charcoal)”). Another kind of commercial area is also
indicated using cells marked as “Commercial (Charcoal)”. These are areas
with commercial establishments specializing in grilling foodstuffs
highlighted as a possible specific source of PM2.5 emissions. The
Pampanga River is marked in blue in the figure, and in the southeast a new
residential area near open fields and agricultural areas has been built up.
Note that some of the grid cells are marked as land uses directly: cemetery
and terminal, the latter corresponding to the central transport terminal of
Cabanatuan, where high vehicular emissions are expected.
PM2.5 emission estimation
All calculations that have been used to estimate PM2.5 emissions are
based on a general formula used by the US EPA in the AP 42 Compilation of
Air Pollutant Emission Factors (EFIG, 1995), as shown in Eq. (1)
E=A×EF×1-ER100,
where E is equal to PM2.5 emissions, A is the activity rate/data
(e.g.,
quantity of fuel used, percentage of households using fuel), EF represents
the emission factor, and ER is the overall emission reduction
factor/efficiency in percent, if applicable. In the present method, E is
estimated as being the quantity of PM2.5 per unit cell: micrograms per
0.01 km2 (1 ha) per year. ER refers to other factors affecting the
total amount of PM2.5 emissions (such as factors not directly
accounting towards the quantity of fuel used; ER factors also incorporate
the activity of those using quantities of fuel lower than average). This
comprises the various factors that are also part of activity data (as in,
factors that modify the amount of emissions generated) as used in this
study.
Local emission factors
Emission factors for households, vehicular emissions, and agricultural waste
burning are estimated from various local studies and projects (Table 1). For
households, the study of Cayetano et al. (2014b) is
used as a reference for its PM2.5 emission factor. The emission factors
for vehicular sources and agricultural waste burning are sourced from in-house
laboratory studies.
Data sources for emission factors.
FactorSourceEmission factors for households (charcoal)Cayetano et al. (2014b)Emission factors for vehicular activity (motorcycles/tricycles, jeepneys, PUVs)In-house dataEmission factor for agricultural waste burning (rice straw)In-house data
Data sources for activity data.
FactorSourcePopulation data, land use2016 Comprehensive Land Use Plan (Provisional; CPDO, 2016), 2015 Socio-Economic Profile (SEP; CPDO, 2015)Activity data for households; LPG, charcoal consumption2011 and 2005 Household Energy Consumption Survey (HECS; PSA, 2011); on-site ground surveysActivity data for vehicles: PUVs, motorcycles,and tricyclesLand Transportation Office annual reports(LTO, 2016); on-site ground surveysData on rice production and rice landagricultural area2016 Comprehensive Land Use Plan (Provisional; CPDO, 2016),2015 Socio-Economic Profile (SEP; CPDO, 2015)Data on rice straw generated per amount rice producedBakker et al. (2013)Activity data
Table 2 compiles the sources of activity data used in this study. Household
and population data are obtained from local government documents,
particularly the Comprehensive Land Use Plan(s) (CLUP) and Socio-Economic
Profile(s) (SEP) of Cabanatuan (CPDO, 2015, 2016). Information on total amount of fuel used by household is
obtained from the national Household Electricity Consumption Survey (HECS),
conducted in 2005 and 2011 (PSA, 2011). Data on rice production as an
indicator for agricultural waste production are obtained from the 2015
Cabanatuan SEP. The findings of the study of Bakker et al. (2013) are
used as a reference to calculate how much agricultural waste (rice straw) is
produced per amount of rice produced.
Emission estimation equations
Emissions for household fuel (charcoal) were estimated with the formula
shown in Eq. (2):
Ehouseholds=(Nh×HF)×Qfuel×EF×0.01,
where Nh is the estimated number of households (generated from city
government data), and HF is the percentage of all households using charcoal
as fuel, obtained from the HECS. Qfuel is the quantity of fuel in
kilograms used per year by each household, sourced from the HECS and
verified using sensitivity analysis by ground surveys (see Sect. 2.4). EF
corresponds to the emission factor for charcoal fuel PM2.5 per square
kilometer per year; this is then multiplied by 0.01 to scale to each 0.01 km2 cell.
PM2.5 emissions for vehicular sources were estimated with the formula
shown in Eqs. (3) and (4).
EMC/TC=(Nu×DF×AVF)×(EF×KT×SDF)×0.01EPUV=(Nu×DF)×EF×0.01
Factors that are the same for both equations include the estimated
number of vehicle units (Nu), the density factor (DF; amount of vehicles per
km2), and the emission factor (EF). The in-house emission factor for
motorcycles and tricycles (here abbreviated as MC/TCs) is measured as
PM2.5 per kilometer traveled (per vehicle). Due to this non-standard EF
unit, additional factors are required in Eq. (3). These include the
association vehicle factor (AVF), the percentage of vehicles which are
officially registered and properly accounted for by the city. To scale the
EF to its proper units, it is multiplied by factor KT (kilometers traveled
per day) and SDF (days in service per year). Similar to the previous
example, the total is also multiplied by 0.01 to scale to each 0.01 km2
cell. The DF and AVF were verified using sensitivity analysis by ground
surveys as detailed in Sect. 2.4.
Emissions for agricultural waste burning were estimated with the formula
shown in Eq. (5):
Eagricultural=RSRA×EF×SF,
where RS is the amount of rice straw produced per year, divided by RA,
which is the total area in hectares (0.01 km2) used for growing of
rice. EF is the in-house obtained emission factor for rice straw burning
PM2.5 per year per square kilometer. SF is the survey factor,
representing the percentage of farming area where burning of rice straw as
agricultural waste is used. This reduction factor is taken from the study of
Launio et al. (2013).
List of activity data factors validated by ground survey sensitivity
analysis.
FactorValue before validationValue after validation% deviation (from sensitivity analysis)Household fuels Quantity of (household) fuel used (Qfuel)194 kg yr-1(HECS, 2011)173.3 kg yr-110.7 %Vehicular emissions Kilometers traveled (KT)80 (in-house data)87.219.0 %Days in service (SDF)320 (in-house data)304.44.9 %
These equations are applied to estimate PM2.5 emissions for each cell,
determined by its land cover type (households, vehicles, agricultural).
After the estimated emissions for each cell have been calculated, they were
mapped using ArcMap (ArcGIS 10.1) software (ESRI, 2011). All cells with
estimated PM2.5 greater than zero are plotted for each land cover
type.
Validation of activity data factors (ground surveys and sensitivity
analysis)
Ground surveys were conducted to validate specific activity data factors
used in the PM2.5 estimation process. A total of 98 respondents (32 for
households, 33 for tricycles, and 33 for PUVs) were surveyed for the
validation of activity data factors involving household fuels, tricycle, motorcycle, and PUV
(jeeps/vans) usage. This process was used as a form of sensitivity analysis,
intended as a way to fine-tune these factors to the setting of Cabanatuan. For reference, the sensitivity analysis procedure reported by
proponents of the Clean Air for Smaller Cities project (ASEAN-GIZ) used a
margin of 5 % to determine variability of traffic data collection while
surveying roads for mobile air emissions (Yuberk and Cornet, 2013).
This survey was the source of some of the factors used in the estimation
process. These include the amount and type of household cooking fuel used, registration under a tricycle/PUV riders association,
and kilometers traveled per day per vehicle. While not directly impacting
the study, the usage of gasoline fuels and engine maintenance options was
also surveyed. Table 3 shows the list of activity data factors that have
been validated in this activity.
The respondents that were surveyed were taken from specific areas, termed
emission hotspots. These are locations where the amount of estimated
PM2.5 emissions are expected to be high. From the total estimated
maximum respondents per type (households, vehicles like MC/TCs, PUVs), the
sample group for this study accounts for around 1 % of the total for
respondents for households, around 5 % for total respondents for MC/TCs,
and around 2 % for the total for respondents for PUVs. This proportion of
the sample size is very low, so the proponents have implemented stratified
sampling intended to make the small sample as representative of the entire
study area as possible.
Results and discussion
The resulting maps of the estimated PM2.5 emissions can be seen in
Figs. 5 to 9. As seen in Fig. 5, the cells indicating the locations of
household-related emissions are located in the fringe of the central
residential areas, where households using charcoal as fuel are mostly
situated. High levels of PM2.5 are expected in these areas, with
levels reaching up to 1 kg of PM2.5 per year per cell. As
this map focuses on charcoal as a fuel source, emissions from commercial
establishments using charcoal were also included. Similar to households,
Eq. (2) was also used to estimate the emissions in these areas.
Map of estimated PM2.5 emissions from burning of household
fuels. Base map derived from satellite imagery (Google Earth Pro, 2015, 2016).
Map of estimated PM2.5 emissions from motorcycles and
tricycles. Base map derived from satellite imagery (Google Earth Pro, 2015, 2016).
Map of estimated PM2.5 emissions from PUVs (public utility
vehicles/jeepneys/XLTs). Base map derived from satellite imagery (Google Earth Pro, 2015, 2016).
Map of estimated PM2.5 emissions from burning of rice straw as
agricultural waste. Base map derived from satellite imagery (Google Earth Pro, 2015, 2016).
Map of estimated PM2.5 emissions combining all factors in the
study. Base map derived from satellite imagery (Google Earth Pro, 2015, 2016).
The widespread presence of tricycles in Cabanatuan is made evident in
the map shown in Fig. 6; almost all cells aside from those indicating
non-built-up areas or agricultural areas have assigned values. Due to the
high overall presence of motorcycles and tricycles as a mobile emission
source in the study site, PM2.5 levels are expected to be considerably
high as a factor of total emissions in the city.
Areas of interest concerning the very high density of tricycles and
associated emissions include the central commercial zone, located within the
poblacion barangays of Cabanatuan. A portion of the city center around
the old capitol and the public market has a high density of tricycles
contributing to PM2.5 emissions. High concentrations of PM2.5
emissions can also be seen in major roads extending from this central area. Of notice is an isolated four-cell segment in the southwest corner
of the map; this is an area near a crowded intersection of the national
highway and a road leading to the central transport terminal of Cabanatuan. In addition, this area is also a substantial terminal for tricycles on
its own (such terminals are often referred to in the vernacular as
toda) servicing the immediate vicinity and the growing commercial zone to the
south of the study site.
In contrast, emissions coming from PUVs (map shown
in Fig. 7) are found only on certain routes, as they are usually used for
inter-city transport compared to tricycles. The map indicates emissions from
both jeepneys and buses. Emissions for PUVs
are estimated to be mostly equal along major roads, marked with cells
representative of higher emissions. However, as the number of PUVs servicing
the portion of the city near the study site are not as high as that of the
number of tricycles, the estimated emissions generated from PUVs are
expected to be much lower compared to tricycles.
A factor not usually present in major urban areas is the presence of
agricultural land uses, which are more common in regional centers, especially
those of provincial centers. These land uses characterize cities that
hybridize both rural and urban elements such as Cabanatuan. In this
context, a candidate source of PM2.5 emissions, burning of
agricultural waste, was taken into account in this PM2.5 estimation
study. Agricultural wastes such as rice straw are frequently still burned as
part of a farmland management practice in these regions, an activity that
contributes to harmful emissions of particulate matter.
The map of estimated emissions from rice straw burning is shown in Fig. 8.
The amount of PM2.5 here is assumed to represent the entire year,
despite rice straw only being burned as agricultural waste in certain
seasons. In particular, rice straw burning only occurs at the end of each
planting season. This typically occurs around the months of April and
October in Cabanatuan.
Also, only a certain fraction of all agricultural land in the city is used
in the growing of rice (these data are taken from the Cabanatuan City CLUP),
and this was taken into account when estimating emissions for this map. A
point to note is the fact that nearly all of the cells tagged as
agricultural are only the fringes of larger zones used for this purpose;
larger agricultural areas can be found to the northwest, southeast, and east
of the study site. More importantly, these areas are very close to the city
center itself; it can be observed that the residential, commercial, and
agricultural land uses are located very close to each other, almost
intersecting inside the investigation area.
A map showing combined emissions for all four factors used in this study is
shown in Fig. 9. With the combined contributions visible in this map, areas
of high concentration of PM2.5 emissions become more evident. Both
residential and commercial zones, as well as the dense transportation (by
tricycle) network within the poblacion and the area immediately to its southeast
contribute much of the emitted particulates; definite areas of high
PM2.5 concentrations can be seen in this location, likely from the high
contributions of combustive fuels for both households and agricultural waste
burning.
Summary and conclusion
As seen in the resulting maps, households and vehicular sources (tricycles)
account for much of the total PM2.5 emissions in the Cabanatuan
investigation area. PUVs (jeeps) account for a small portion of vehicular
emissions. PM2.5 from burning of agricultural waste was found to be a
large constituent of total particulates. As the investigation area is only a
small fraction of the entire city, this likely means that agricultural waste
burning is a significant source of PM2.5 in the largely agricultural
Cabanatuan. This is open to future research on air quality management
in the city, among others.
The amount of PM2.5 emissions in the investigation area estimated by
this method is comparable to emission levels in urban metropolitan areas. A
possible reason for this is the common usage of biomass-based fuels such as
charcoal or the high levels of particulates from vehicular sources.
Vehicular emissions and agricultural waste burning, at their highest levels,
are responsible for emission levels of at least 2 kg of PM2.5
per 1 ha cell per year each. This interface between rural and urban
land uses in Cabanatuan has produced a varied environment for research
on multiple areas. Household fuel usage, vehicular sources, and agricultural
waste burning are a major component of the city's air pollution and more
research on its management in the region is necessary.
The validation of specific activity data factors is effective at adapting
them closer to the specific conditions present in Cabanatuan. While the
more general original in-house values are more appropriate in areas like
Metro Manila, the validation procedure has made them more appropriate for
smaller cities in general. An issue during the ground survey activity
involves its small sample size compared to the possible maximum number of
respondents in the investigation area. However, the benefits of fine-tuning
the activity data with this analysis outweigh its disadvantages. Also, in
future researches, the ground survey and sensitivity analysis validation
will highly be improved if the sample size is greatly increased.
Recommendations
As stated earlier, this method for the estimation of PM2.5 emissions
is intended for use by local government stakeholders for smaller cities and
regional centers in any study country. While this method was primarily
developed to estimate PM2.5, similar methods can be used for other
components of the emission inventory process in the country (i.e., criteria
pollutants and greenhouse gases).
Ground verification of surface features is necessary to ensure the accuracy
of land cover maps. Due to this, the researchers recommend a detailed field
survey on the ground level with surveyors equipped with GPS units to ensure
that the gathered information on surface features is up to date. This will
also provide a way to offset the possible inaccuracy of the Google Earth
satellite image in terms of its coordinates.
This method currently compiles estimated emissions on a yearly basis. The
presence of seasonal factors such as agricultural waste burning, however,
can indicate the possible usefulness of seasonal mapping of PM2.5
emissions. Since it is equally important to investigate air pollution
emissions through different temporal scales, such an option is worth looking
into in the future.
Additionally, a method for the verification of activity data factors,
similar to this study's sensitivity analysis, is highly recommended for
future studies. A focus on such studies but on a much larger scale (a ground
survey that represents a much larger portion of an investigation area) would
be instrumental in placing the total emission estimate more accurate with
regards to specific conditions in a city. Actual in situ measurement of PM2.5
emissions is also possible for a small study area like this one. Such a
validation activity would require the use of air samplers or particle
counters to actually measure the amount of PM2.5 present. A
limitation of this method, however, lies in the fact that it can only
measure total particulate emissions and cannot differentiate between
different sources of PM2.5. Because of this, any follow-up study that
involves actual in situ PM2.5 measurements must also include chemical analysis
of sampled particulates as well as source apportionment in order to
determine the actual amounts of air pollutants by source. A reference study was conducted by Cayetano et al. (2014a), and research
projects with methodologies similar to this study are currently being conducted in Cabanatuan and other cities.
Lastly, as this method is primarily geared towards the estimation of
particulate emissions, the planning of mitigation strategies to increase air
quality in target cities such as in Cabanatuan must also be pursued in
tandem with emission inventories conducted by the local government and other
stakeholders. Local governments in the Philippines are continuously
upgrading their capabilities for spatial knowledge and city planning due to
the propagation of usage of GIS software by government officials and
non-governmental organizations (NGOs). This particular study has used ArcGIS,
a proprietary software that requires a paid license, which may prove to be
an issue for units with small financial capabilities. As this method can
just as easily be executed using free and open-source GIS software such as
QGIS, studies using this software may be used in the future for
organizations seeking a less costly alternative for GIS. The specialization
of city environment officers in pollution studies is a process that is both
ongoing and needing more attention. For future studies and efforts, it will
be worthwhile to increase the capability of local stakeholders to plan for
environmental issues like air pollution.
Excel spreadsheets containing the calculations of the estimated
PM2.5 emissions, as well as those used as attribute tables for GIS mapping, are attached as the Supplement of this paper.
The Supplement related to this article is available online at https://doi.org/10.5194/amt-10-3313-2017-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
This study was supported by research grants from the Natural Sciences
Research Institute (2016-ESM-001) and the Office of the Vice Chancellor for
Research and Development (141406 PNSE), both from the University of the
Philippines, Diliman. The study was also supported by the Ministry of
Science, ICT and Future Planning in South Korea through the International
Environmental Research Center and the UNU & GIST Joint Programme on
Science and Technology for Sustainability from 2014 to 2016. The authors would
like to acknowledge the local government of Cabanatuan, the Office of
the City Mayor, the City Planning and Development Office, and the
Environmental Protection Division for their assistance in the activities
conducted for the purposes of this study.
Edited by: Marloes Penning de Vries Reviewed by: two anonymous
referees
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