INTRODUCTION
Research Rationale
Urbanization began approximately 10,000 years ago when hunter-gatherers adopted early farming techniques, leading to the establishment of semi-permanent settlements rather than a nomadic lifestyle in search of food.
As territories expanded and trade increased, more individuals were attracted to trade centers due to the availability of job opportunities Urbanization has evolved over time, driven by technological advancements, transforming into the dynamic modern cities we see today (Kite, 2013).
Urbanization is driven by significant demographic and structural shifts, leading to complex transformations that extend beyond simply converting rural land to urban spaces This multifaceted process impacts various aspects of a community, including economic, social, technological, demographic, political, and environmental dimensions (Stelter & Artibise).
Urbanization leads to the development of densely populated urban areas characterized by extensive infrastructure, including railways and skyscrapers, which support residential, commercial, and industrial activities while minimizing agricultural practices (National Geographic Society, 2011) It is projected that by 2030, approximately 60% of the global population will reside in these urban environments (Yadav, 2017).
It was reported by the World Bank Group in 2017 that Philippines is one of the fastest urbanizing countries in the East Asia and Pacific region About 50 million Filipinos
By 2050, the urban population is expected to reach 102 million, highlighting the urgent need for land conversion for residential, commercial, and industrial purposes This escalating demand for land inevitably leads to significant environmental challenges, including increased waste generation and air pollution, resulting from ongoing changes in land use and land cover.
Sta Rosa City, recognized as the fastest growth center in the Philippines, is located in the dynamic region of South Luzon As a first-class component city in Laguna, it covers a land area of 5,543 hectares and is situated 40 kilometers south of Manila, the capital The city is adjacent to Laguna Lake, the largest lake in the Philippines, and is bordered by Biñan to the northwest, Cabuyao to the southwest, Cavite to the west, and Laguna de Bay to the northeast.
It became a first-class municipality in 1993 and officially became a city in 2004 which was considered as an economic success
(http://www.santarosacity.gov.ph/investment-profile/) The city has now evolved into a major residential, industrial, and commercial center Figure 1 shows the location of the study area
Effective monitoring of land use and land cover changes is crucial for urban planners at both local and regional levels Utilizing efficient detection and analytical techniques allows for a comprehensive assessment of urbanization patterns and trends.
Sta Rosa City has been extensively studied regarding the effects of urbanization, particularly concerning flooding This research aims to provide an updated analysis of land use and land cover changes over the past 24 years, highlighting the connections between rapid urbanization and its environmental and socio-economic impacts, including waste generation, air pollution, population growth, and the economic activities within the city.
In 2017, the analysis of waste generation, air quality, and socio-economic factors was conducted using Landsat data, enhanced by modern technologies like Remote Sensing and Geographic Information Systems Additionally, secondary data from various sectors were utilized to provide a comprehensive understanding of these issues over different years.
Figure 1: Map of Sta Rosa City Divided Into Eighteen Barangays
Research Objectives
The study generally aims to assess the environment and socio-economic impacts of urbanization in Sta Rosa City Specifically it aims to:
1 To estimate the rate and extent of urbanization in Sta Rosa City from 1993 to 2017;
2 To identify land use/land cover which has undergone major conversion from 1993 to 2017;
3 To find out significant relation of waste production to urbanization of Sta Rosa City;
4 To analyze the occurrence and concentration of PM 2.5 and PM 10 in Sta Rosa’s air quality from 2017;
5 To understand the trends of population, migration, and economic activity/status of Sta Rosa City
Statement of the Problem
Rapid urbanization is a significant contemporary issue, often leading to unplanned growth and urban sprawl, which raises concerns about dramatic changes in land use and land cover (LULC) According to Reis (2008), alterations in LULC due to anthropogenic activities have detrimental effects on climate patterns, natural hazards, and socio-economic conditions.
The City of Santa Rosa has emerged as a key residential, commercial, and industrial hub in South Luzon, one of the Philippines' most dynamic sub-regions While rising urbanization fosters economic growth by creating job opportunities and boosting incomes, unchecked rapid urbanization can lead to negative consequences, including overcrowding, significant land use changes, increased waste generation, and heightened pollution levels.
Hence, the study will be conducted to answer these questions:
1 What is the rate of urbanization in Sta Rosa City from 1993 to 2017 in terms of land area conversion?
2 Which land use (forest/trees, agricultural land, and bare land), changed the most from initial state to a final state of built-up from 1993 to 2017?
3 What is the relationship of total waste generation and population of Sta Rosa City in 2015?
4 What are the significant trends of the PM concentration of Sta Rosa City in 2017?
5 How did urbanization affect socio-economic factors such as population growth, population shift, and economic status of Sta Rosa City?
Significance and Limitations of the Study
Urbanization and the expansion of urban areas often stem from development plans aimed at economically supporting a growing population, yet these plans frequently overlook critical factors like climate patterns and natural hazards (Iizuka et al., 2017) The reliance on remotely sensed data has become increasingly important for assessing the effects of urbanization on land use and land cover, especially given the challenges of conducting field surveys If not properly monitored and analyzed, the changes in land use and land cover could lead to significant adverse impacts in the future.
Understanding the current extent and rate of urbanization, along with the patterns of land use changes and associated issues like waste generation and air pollution, is crucial for urban planners and policymakers in Sta Rosa City This study will also serve as a valuable baseline for other researchers interested in urbanization and its socio-economic impacts.
This study utilized free downloadable Landsat satellite data, featuring a resolution of 30 meters by 30 meters, alongside shapefiles for administrative boundaries, to analyze land use and land cover changes However, the research faced challenges in acquiring Landsat images from the same months due to the limited availability of cloudless data for Sta Rosa.
Urbanization mapping has primarily concentrated on land use and land cover, often overlooking essential physical characteristics of urban features, including residential, industrial, and commercial buildings, as well as elevation and soil characteristics.
The study aimed to identify the quantity, main composition, and major sources of solid waste generated in Sta Rosa City, highlighting the significant relationship to urbanization However, it relied solely on secondary data from 2015 provided by the City Environment and Natural Resources, as conducting a field survey was not feasible due to the study area's distance and time constraints.
The study on the impact of urbanization on air quality in Sta Rosa City focused solely on PM 2.5 and PM 10 levels, utilizing data from a single roadside air quality monitoring system This data, sourced from the Department of Natural Resources – Environmental Management Bureau in Region IV-A CALABARZON, allowed for an analysis of PM 2.5 and PM 10 trends in 2017, rather than the intended creation of an air quality index map for the city Additionally, the research examined socio-economic factors such as population, migration, and employment status using available secondary data and censuses.
Definition of Terms
Definition of Terms can be found in Appendix 1
LITERATURE REVIEW
Urbanization and Land Use/ Land Cover Change
As of now, 55% of the global population resides in urban areas, and this figure is projected to rise to 4.9 billion by 2030 In contrast, the rural population is expected to decline by 28 million between 2005 and 2030 (Bhatta et al., 2010) Asia, despite having a lower level of urbanization, accounts for 54% of the world's urban population (UN, 2018) Notably, the Philippines is among the fastest urbanizing nations in the East and Asia Pacific, with cities occupying 2-5% of its total land area (World Bank Group, 2017).
Urbanization, as defined by the United Nations, refers to the growing percentage of the population residing in urban areas and the development of cities due to the permanent concentration of large groups of people in limited spaces.
Urbanization is a global phenomenon that varies across countries and their development levels, with wealthier nations already experiencing significant urban populations, while developing countries remain predominantly rural but are expected to urbanize more rapidly (United Nations, 2018) This process involves a physical transformation of landscapes, shifting from natural land covers to developed areas characterized by impervious surfaces for residential, industrial, and commercial uses, along with essential facilities like schools and hospitals (Sharma, 2014).
Land, an irreversible resource is central to primary production systems (Prasad
& Sreenivasulu, 2014) Land cover is the physical characteristics of the surface of the
Land use refers to the human activities that occur on the Earth's surface, encompassing both manmade features like housing and settlements, as well as natural elements such as vegetation, water bodies, and soil Understanding land use is crucial for effective management of these features and their impact on the environment.
Land use and land cover are closely related concepts often used interchangeably, as land cover results directly from land use LULCC refers to the human alteration of Earth's terrestrial surface, making it crucial for understanding the Earth as a system These elements are vital for effective planning and management activities, as they play a significant role in evaluating environmental issues, population shifts, and economic conditions.
Impacts of Urbanization to Environment and People
Land use and land cover changes significantly impact the three dimensions of sustainable development: economic, social, and environmental Wu (2008) identifies these changes as a primary driver of environmental degradation However, Rawat et al (2013) suggest that if managed properly, land use changes can promote economic growth The rapid urbanization and expansion of cities, fueled by population growth and increased economic activities, offer benefits for economic development Nevertheless, important factors such as climate patterns, natural hazards, and sanitation are often neglected in this process.
12 expanding cities which eventually leads to land, air, and water pollution, traffic congestion, overcrowding, increased temperature, diseases, etc
The land use and cover in Sta Rosa City have undergone significant changes since 1946, especially after 1990 when urban development surged and agricultural areas diminished In 1946, a substantial part of the city was dedicated to agriculture, with farmers cultivating crops like rice, corn, coffee, and sugarcane, while also engaging in livestock farming and fishing in lakeshore communities The establishment of the South Luzon Expressway (SLEX) in the 1980s attracted local and foreign investments, leading to rapid urbanization and a decline in agricultural lands and livelihoods Consequently, Sta Rosa City has negatively impacted the Class C status of Laguna Lake.
Flooding in Sta Rosa City, particularly during Typhoon Ondoy in 2009, highlights the environmental impacts of urbanization, primarily caused by the accumulation of unmanaged solid waste from residential areas and informal settlers, which obstructs canals and waterways Additionally, the increasing demand for water driven by economic activities has led to significant groundwater depletion.
The increasing population and severe use of fertilizers and pesticides are significantly contributing to the declining productivity of Laguna Lake This decline is exacerbated by industrial pollution, siltation, sedimentation, shoreline encroachment, and the rapid conversion of prime agricultural lands into industrial and residential areas Consequently, fish growth and harvests are diminishing due to these environmental challenges (Eugenio, 2018).
The rapid population growth, unchecked urbanization, and industrialization in developing countries have led to a significant increase in waste generation and inadequate waste management (Ugwuanyi & Isife, 2012) This surge in waste, coupled with poor disposal practices, has detrimental effects on environmental sustainability, resulting in pollution, space shortages, waste leaching, groundwater contamination, blocked drainage systems that exacerbate flooding, and the spread of diseases like cholera and dengue fever due to pests attracted to waste The U.S Public Health Service has identified 22 diseases linked to improper solid waste management (Pervez & Ahmade, 2013) Consequently, there is a pressing need for heightened awareness and action regarding solid waste management on a global scale.
In the Philippines, with the rapid growth of population, waste management has become a major environmental challenge (Castillo & Otoma, 2013) About 35,580
The Philippines generates 14 tons of garbage daily, highlighting a significant waste mismanagement issue that led to the enactment of Republic Act (RA) 9003, known as the Ecological Solid Waste Management Act of 2000 This law establishes an ecological solid waste management program, creates institutional frameworks, prohibits certain actions, imposes penalties, and allocates funding Solid waste is defined by Sta Rosa City’s Environmental Code as all discarded household, commercial, and non-hazardous industrial waste, including street sweepings and construction debris In 2015, the average waste generation per person was 0.7 kg per day The rising waste from densely populated areas and illegal dumping into waterways has significantly contributed to severe flooding in the city (Eugenio, 2018).
Urban development has led to a rise in human-made structures like buildings and factories, contributing significantly to air pollution This environmental issue is closely linked to urbanization and changes in land use Industrial emissions and vehicle exhausts are identified as primary sources of air pollution, highlighting the impact of urban infrastructure on air quality Consequently, the built environment plays a crucial role in exacerbating air pollution due to increased human activities.
Air pollutants significantly impact the surrounding environment and include both gaseous substances and particulate matter, such as PM 2.5 and PM 10 Additionally, certain land uses can indirectly contribute to air pollution through vehicular emissions (Xu et al., 2016).
Particulate matter (PM), also known as particle pollution, includes PM 2.5 and PM 10, and is a significant air pollutant influenced by human activities and environmental factors (EPA, 2016; Chan & Yao, 2008) This term refers to a mixture of solid particles and liquid droplets present in the air, with sizes ranging from visible dust and soot to microscopic particles (EPA, 2016) PM 2.5, consisting of fine particles with diameters of 2.5 micrometers or smaller, is known to contribute to smog and aerosol formation at high concentrations (Xing et al., 2016) and can penetrate the lungs and cardiovascular system (DENR, 2017) In contrast, PM 10 refers to larger particulate matter.
Coarse particles measuring 10 micrometers or smaller are generated from various combustion sources, including motor vehicles, power plants, residential wood burning, forest fires, agricultural burning, and certain industrial processes (AirNow, 2017).
Air pollution poses a significant environmental health risk, affecting 92% of the global population in areas that surpass the World Health Organization's air quality guidelines In the Philippines, this crisis contributes to one in four deaths, highlighting the urgent need for improved air quality measures.
Air pollution is a significant concern in Sta Rosa City, primarily due to the rising number of industries and worsening traffic congestion Residents living near industrial areas face increased health risks To address this issue, the city began monitoring ambient air quality in 2016 by installing an air monitoring station, as initiated by the Environmental Management Bureau.
Republic Act 8749 mandates the monitoring of ambient air quality in major cities across the country To achieve this, the Department of Environment and Natural Resources – Environmental Management Bureau has established regional monitoring systems tailored to different pollutants These systems include general air pollution monitoring for fixed locations and roadside air quality monitoring to evaluate pollution from heavy traffic.
Remote Sensing and Geographic Information System
Remote sensing is the science of collecting information about an object or area from a distance using instruments like sensors (NOAA, 2017) This term is often associated with studying the Earth system and its dynamics (European Space Agency, 2010) Serving as the 'eye' of the planet, remote sensing provides repeated and comprehensive images of the Earth from aerial or above-ground perspectives.
Remote sensing has significantly advanced the characterization of spatial and temporal analysis of urban growth patterns and processes through the use of multi-stage images (Lillesand & Kiefer, 2000; Sun et al., 2007) Additionally, Geographic Information System (GIS) serves as a comprehensive framework for collecting, storing, processing, and modeling various types of geographical data, effectively illustrating landscape structure, function, and change (Sudhira et al., 2004).
The integration of Remote Sensing (RS) and Geographical Information System (GIS) has significantly enhanced the understanding of various research fields, as highlighted by Vallesteros (2002) This combination has led to substantial advancements in mapping land use and cover changes, making the data more comprehensive and valuable Furthermore, it has increased public awareness of the interconnectedness and vulnerability of Earth's elements, underscoring the crucial role of RS and GIS in gathering, processing, managing, and modeling information about our planet (Lillesand & Kiefer, 2000).
MATERIALS AND METHODS
Materials
3.1.1 The objects of the research
Land use/ land cover change analysis
Classified images of Sta Rosa City in 1993, 2005 and 2017
Land use/ land cover change map of Sta Rosa City in 1993-2017
Graphs and Tables of air quality and socio-economic factors
Conceptual Framework
The conceptual framework for analyzing land use and land cover changes in Sta Rosa City is illustrated in Figure 2, which outlines the methods employed in this analysis Additionally, Figure 3 details the procedures used to create maps for population distribution and solid waste generation in the city This article will elaborate on these methods, covering everything from data collection to result analysis.
Land use/ land cover change map
Figure 2: Conceptual Framework of Land Use/ Land Cover Change Analysis
Land Use/ Land Cover Change Mapping
The land use and land cover change map of Sta Rosa City was created using data sourced from the United States Geological Survey EarthExplorer (USGS EE) website, which archives satellite images The analysis utilized free and downloadable Landsat images to ensure accurate representation of the area's changes.
The study utilized five images from Landsat 5 TM and Landsat 8 OLI/TIRS, focusing on the years 1993, 2005, and 2017 Due to the scarcity of cloudless data in Sta Rosa City, it was not feasible to select images from the same month across all years Specifically, Landsat 5 TM images were captured in May for both 1993 and 2005, while the Landsat 8 OLI/TIRS image was taken in June 2017.
Figure 3: Conceptual Framework for Spatial Maps
In the Philippines, the dry season starts in November and ends in May, while rainy season begins in June and ends on October (see table 1)
Image pre-processing is crucial for enhancing raw satellite data, which often suffers from defects like distortions, low brightness, and haziness Effective pre-processing is essential for accurate detection and analysis of land use and land cover changes, as highlighted in various studies (Rawat & Kumar, 2015) The specific corrections required depend on the nature of the errors present in the raw data.
Radiometric errors arise from atmospheric influences and variations in sensor functions that capture data The raw data or images consist of digital numbers (DN) representing electromagnetic radiation per pixel recorded by sensors These DN values can be transformed into real-world units such as radiance, reflectance, or brightness temperature (Humboldt State University, 2015) Therefore, radiometric calibration is crucial for accurate interpretation of images.
Table 1: Collected Satellite Images and their Attributes
Spacecraft ID Sensor ID Date Acquired Spatial
Landsat 8 OLI/TIRS June 6, 2017 30 meters 9/10
22 different sensors are used It is also important before applying atmospheric correction so that DN values are converted to TOA or top-of-atmosphere reflectance
Atmospheric correction is essential for minimizing the impact of atmospheric scattering caused by clouds, aerosols, and gases The image processing software ENVI provides effective tools for data calibration For Landsat 8 OLI/TIRS in 2017, the Dark Object Subtraction (DOS) technique was utilized, which is a widely used method for atmospheric correction in ENVI Additionally, the Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) from ENVI's Atmospheric Correction Module was applied to Landsat 5 TM data from 1993 and 2005 Results of these pre-processing methods are illustrated in Figure 4.
Figure 4: Pre-processed Images of (a) 1993, (b) 2005, (c) 2017 in Natural Color Combination (Bands 321 for Images a and b; Bands 432 for Image c
Using ArcMap, the images were cropped to fit the administrative boundary of Sta Rosa City, with the shapefile sourced from PhilGIS.org, a platform offering free GIS data for the Philippines The shapefile, dated 2011, represents a total land area of 4,764.78 hectares.
The article presents subset images of the study area for various years, utilizing natural color band combinations: bands 321 for Landsat 5 TM and bands 432 for Landsat 8 OLI/TIRS Detailed band designations for both Landsat 5 TM and Landsat 8 OLI/TIRS are available in Appendices 2 and 3.
Figure 5: Clipped Images of the Study Area - Santa Rosa City for years 1993, 2005 and
Image classification involves simplifying spectral information into interpretable categories, utilizing two main methods: unsupervised and supervised classification Unsupervised classification automatically groups pixels based on their spectral characteristics without user input, while supervised classification relies on user-defined training samples to categorize images into classes such as built-up areas, forests, agricultural land, and idle land, requiring at least 20 samples per class Various band combinations, including bands 432 for Landsat 5 TM and bands 543 for Landsat 8 OLI/TIRS, along with the Normalized Difference Vegetation Index (NDVI), were employed to enhance classification accuracy, as illustrated in Figures 6a and 6b The Maximum Likelihood Classification (MLC) algorithm was utilized for supervised classification, recognized for its effectiveness and satisfactory accuracy in land use and land cover (LULC) change assessments, as noted by Kamrul et al in 2018.
Figure 6(a) and (b): False color composite (FCC) and Normalized Difference Vegetation
Index (NDVI) for 1993, 2005, and 2017 Images
Accuracy assessment is crucial for evaluating the reliability of classification results and their alignment with real-world conditions This process involves comparing classified images to accurate reference data, known as ground truth data In this study, fifty random points were generated for each class in the images, totaling 200 points for the years 1993 and 2005.
In 2017, ground truth data was gathered from mid-high resolution imagery accessed via Google Earth Pro, alongside previously classified imagery and GIS data layers The accuracy assessment identified classification errors by calculating user’s accuracy, producer’s accuracy, overall accuracy, and Cohen Kappa’s coefficient, which were compiled into error matrices.
User accuracy reflects the perspective of the map user and indicates the reliability of how frequently a specific class on the map is expected to occur in the real world.
User accuracy is calculated by dividing the number of correct classifications for a specific class by the total number of instances in that class, also known as the ground truth total Conversely, commission error, which reflects user accuracy, is determined by subtracting the user accuracy percentage from 100% Therefore, a higher commission error indicates a lower level of user accuracy.
Producer's accuracy reflects the viewpoint of the map creator, indicating the frequency with which real-world features are accurately represented on a classified map This metric is calculated by dividing the total number of correct classifications by the total number of actual features present.
Producer's accuracy is inversely related to omission error, calculated by subtracting the producer's accuracy of a class from 100% A higher omission error indicates lower producer's accuracy, highlighting the importance of accurate classification in assessing performance.
Overall accuracy measures the percentage of correctly classified reference sites out of the total, calculated by dividing the number of accurate classifications by the total number of reference sites While it is straightforward to compute and comprehend, it offers only fundamental insights into accuracy The maximum overall accuracy rate is 100%, indicating that every reference site has been classified correctly.
RESULTS AND DISCUSSION
Results of Land Use/Land Cover Change Detection Analysis of Sta Rosa City
Table 2 presents the identified land use and land cover classes, while Figure 7 showcases the classified images of Sta Rosa City The accuracy assessment results, detailed in appendices 4, 5, and 6, indicate a substantial to almost perfect agreement with the reference images Notable changes in land use and land cover are evident, with agricultural land dominating in 1993, transitioning to idle/grassland in 2005, and ultimately to built-up areas by 2017 The built-up regions are primarily located in the northeast, where most residential areas are found, and in the southwest, which houses the industrial zone (santarosacity.gov.ph).
Table 2: Description of the Land Use/Land Cover Classification Used in the Study
Built-up Composed of residential, commercial, industrial areas, highways and roads Forest/Trees Tree patches/forests, Agroforesty
Agricultural land Cultivated and tilled areas
Idle/grassland Open areas, uncultivated or unimproved areas
Figure 7: Land Use/Land Cover Classification of Sta Rosa City in 1993, 2005, and 2017
The analysis of land use and land cover changes was conducted using ArcMap's built-in software, revealing significant statistics in Tables 3 and 4 From 1993 to 2005, there was a notable increase of 10.85% in built-up areas, which further escalated to 44.16% in subsequent years.
From 1993 to 2005, agricultural land in Sta Rosa city decreased by 43.56%, with a further decline of 49.74% from 2005 to 2017 Historically, since the 1960s, these lands were primarily used for sugarcane cultivation However, in the 1990s, many agricultural lands were sold to real estate developers, leading to their transformation into idle lands awaiting development Consequently, idle and grassland areas increased by 50.29% from 1993 onwards.
2005 and decreased (-15.90%) from 2005 to 2017 The decrease in land area of idle lands during this period is attributed to the conversion of these areas into residential and industrial areas
From 1993 to 2005, forested areas and patches of trees decreased by 12.39%, while from 2005 to 2017, these areas saw a significant increase of 70.04% This decline in agricultural land and forest cover during the earlier period was largely due to the rapid urban development in Sta Rosa City, which achieved an income of 54.2 million pesos in 1993 and was designated as a first-class town.
In 2005, the city emerged as the fourth largest in Laguna, following Calamba City, San Pedro, and Biñan City The notable rise in forested areas by 2017 can be linked to various city initiatives aimed at reducing idle land, including tree planting, regulating tree cutting, the adopt-a-lot project, and promoting urban agriculture and vegetable gardening (SEPP Santa Rosa 2013).
Table 3: Land Use/Land Cover Change Statistics of Sta Rosa City from 1993-2017
The Land Use/Land Cover Change Map from 1993 to 2017 illustrates significant shifts in land use, with distinct color shadings representing various categories Notably, a transition from agricultural land to idle land is evident in the southern region of Sta Rosa City, where substantial idle land holdings are present, indicating potential for future development.
Table 4: Changes of Sta Rosa City from 1993-2017
To identify the land use and land cover types that have most significantly contributed to built-up conversion, the rate and extent of these changes are detailed in the accompanying table.
5 It can be inferred that built-up dominated the city of Sta Rosa at 16.18% and out of all the three land use/land covers (forest/trees, agricultural land, idle/grassland), agricultural land has the biggest (10%) share of conversion to built-up Sta Rosa City used to be a sleepy agricultural community prior to its conversion starting in the 1990s Agricultural lands (both sugarcane and irrigated rice fields) have been continuously converted into residential, industrial and commercial areas, now the city
Figure 8: Land Use/ Land Cover Change Map of Sta Rosa City from 1993-2017
As of 2014-2015, only 10% of Sta Rosa's land is dedicated to agricultural rice fields, according to SEPP Known as the "bedroom area" of Metro Manila, many residents in the city's upscale subdivisions commute to work in Metro Manila during the day and return home to Sta Rosa at night.
Total Waste Generation and Waste Composition of Household and Non-
In 2015, Sta Rosa City’s urban barangays generated various types of waste, including biodegradable, recyclable, residual, and special wastes According to Table 6, Brgy Kanluran, with a smaller population, contributed the least total waste and its respective composition, whereas Brgy Pooc, being the most populated area, generated the highest total waste and its associated types.
Table 5: Rate and Extent of LULC from 1993-2017
Land Use/Land Cover Change from 1993-2017 Area (Ha) (%)
Forest/Trees to Built-up 108.65 2.28
Forest/Trees to Agricultural Land 81.44 1.71
Forest/Trees to Idle Land 191.49 4.03
Agricultural Land to Built-up 479.58 10.08
Agricultural Land to Forest/Trees 343.99 7.23
Agricultural Land to Idle Land 750.01 15.77
Idle Land to Built-up 474.77 9.98
Idle Land to Forest/Trees 201.51 4.24
Idle Land to Agricultural Land 158.67 3.34
Table 6: Total Waste Generation, Waste Composition, and Population of Each
Total Waste Generation (kgs/day)
In 2015, Sta Rosa City generated a significant amount of household waste, with biodegradable materials making up 53.60% of the total This was followed by residual waste and recyclable items, while special waste constituted the smallest portion at just 0.26%.
Table 7 highlights the population and waste generation statistics for each barangay, revealing that Brgy Pooc, with a population of 41,542, produced the highest total waste at 16,217.38 kgs/day In contrast, Brgy Sto Domingo, the least populated at 3,935, generated a lower total waste of 3,772.11 kgs/day but had the highest per capita generation rate at 0.96 kg, surpassing the average of 0.7 kg Additionally, Brgy Kanluran recorded the lowest total waste generation, while Brgy Malitlit had the lowest per capita generation, likely due to their primary sources of livelihood and other activities influencing waste production.
Biodegradable Recyclable Residual Special Waste
Figure 9: Composition of Total Waste Generation from Household Sources of Sta Rosa City in 2015 Presented in
In 2015, spatial maps illustrating total waste generation and population distribution in Sta Rosa City were created to provide a visual representation of these factors across each barangay, as shown in Figures 10(a), 10(b), and 11.
Table 7: Population, Total Waste Generation, and Per Capita Generation of
Figure 10(a) and (b): Choropleth maps of Total Waste Generation and Population of Sta Rosa City in 2015
Figure 11: Associated Choropleth Maps of Total Waste Generation and Population of
In 2015, a correlation analysis was conducted to assess the relationship between the population and total waste generation in Sta Rosa City The findings, represented in Figure 12, show a strong positive correlation (r = 0.88) between these two variables, indicating that as the population increases, total waste generation also rises Additionally, the p-value of 0.00000117, which is significantly lower than the α = 0.05 threshold, confirms the strong and positive relationship between population and waste generation in Sta Rosa City for that year.
To tal W aste G en er atio n ( k g s/d ay ) 2 0 1 5
Figure 12: Scatter Plot Showing the Correlation between Population and
In the 2015 Waste Analysis and Characterization Study (WACS) of Sta Rosa City, it was found that non-household waste sources include commercial, institutional, industrial, healthcare, and others Notably, commercial sources generated the highest amount of waste, totaling 79,195.38 kg per day, surpassing all other non-household categories in the city.
2015 which can be attributed to the increasing economic activity in the city
Table 8: Total Waste Generation of Non-Household Sources of Sta Rosa City in 2015
Overall, Sta Rosa City generated wastes at a rate of 246,570.086 kg/day in
In 2017, waste generation in Sta Rosa City is projected to rise to 265,811.18 kg per day, with the highest recorded per capita generation at 0.96 kg, surpassing the average of 0.7 kg Household sources are the primary contributors to this total, accounting for 161,323.66 kilograms of waste An analysis of classified images from 1993 to 2017 reveals a significant increase in built-up areas, particularly concentrated in the northeast section of the city, where most residential zones are located This growth in population and waste generation has resulted in waste being collected and disposed of in the nearby municipality of San Pedro, Laguna.
In 2008, the City ENRO reported an expenditure of 54 million pesos annually, highlighting the waste management challenges faced by local barangays Interactions with barangay officials revealed that residents often resort to scrapping waste, which is then sold to junk shops scattered throughout the city and residential areas While this practice is seen as a means to reduce waste, the conditions surrounding these junk shops are detrimental to the environment and pose health risks to both the scrappers and nearby households.
4.2.2 Particulate Matter (PM 2.5 and PM 10) Concentration of Sta Rosa City
In 2017, the roadside air quality of Sta Rosa City was assessed by measuring particulate matter concentrations (PM 2.5 and PM 10) The analysis revealed that both PM 2.5 and PM 10 levels peaked in June, reaching concentrations of 76.87 µg/Nm³ and 79.61 µg/Nm³, respectively, while January recorded the lowest levels at 2.07 µg/Nm³ and 2.16 µg/Nm³ These variations are likely influenced by climatic factors such as temperature, rainfall, and wind speed A correlation analysis indicated that only the relationship between PM 2.5 and the monthly average wind speed was statistically significant.
In 2017, Sta Rosa City recorded the highest average concentration of PM 2.5 in June, coinciding with the lowest average wind speed for that month A correlation analysis revealed a strong negative relationship between PM 2.5 concentration and wind speed, with a correlation coefficient of r = -0.57 This indicates that as wind speed decreases, PM 2.5 levels increase, and vice versa Additionally, the p-value of 0.051036, which is less than or equal to the significance level of α = 0.05, confirms a significant and strong negative correlation between wind speed and PM 2.5 concentration.
Figure 13: Monthly Average of PM 2.5 and PM 10 Concentrations in Sta Rosa City in
2017 expressed in micrograms per cubic meter
Table 9: Monthly Average of Wind Speed and PM 2.5 Concentration of Sta Rosa City in 2017
Mo n th ly A v er ag e o f W in d Sp ee d in 2 0 1 7
Figure 14: Scatterplot Showing the Monthly Average of PM 2.5 and Wind
Speed of Sta Rosa City in 2017 r = -0.57 p-value = 0.051036
According to Chapter 2, Section 12 of Republic Act 8749, the ambient air quality guideline values are set at 60 µg/Nm³ for PM 10 and 15 µg/Nm³ for PM 2.5, based on US EPA standards In 2017, Sta Rosa City recorded an annual mean of PM 2.5 at 34.12 µg/Nm³, which surpassed the US EPA guideline by 19.13 µg/Nm³ Conversely, the annual mean of PM 10 in the same year was 42.45 µg/Nm³, remaining below the national ambient air quality guideline.
N A A Q G V & U S E P A S T A R O S A C I T Y Annual Mean PM 2.5 Annual Mean PM 10
Figure 15: Comparison of Annual Means of Guideline Values
(from NAAQGV and US EPA and Sta Rosa City’s PM 2.5 and
4.2.3 Population and Population Change, and Economic Activity of Sta Rosa City
Since 1990, Sta Rosa City has experienced significant population growth, with the total reaching 353,767 by 2015, up from just 94,719 in 1990, according to the Philippine Statistics Authority This threefold increase in population over 25 years has led to a rise in waste generation, exacerbating environmental degradation and highlighting solid waste management as a critical concern for the city's physical environment.
Figure 16: Population of Sta Rosa City from 1990-2015
From 1980 to 2013, Sta Rosa City experienced a steady rise in the number of commercial and industrial establishments This growth, particularly from 1980 to 2010, resulted in the creation of approximately 100,000 jobs and significantly boosted the country's export earnings by nearly $8 billion.
The construction of the South Luzon Expressway (SLEX) has significantly contributed to the rapid development of Sta Rosa City, attracting both local and foreign investors This growth has led to the establishment of major companies, including the largest Coca-Cola bottling plant in Southeast Asia and notable Japanese firms like Fujitsu Ten, Isuzu, and Honda, as well as automotive giants such as Toyota, Nissan, and Ford As a result, Sta Rosa City has earned the title of the Automotive Capital of the Philippines While the influx of commercial establishments has created numerous job opportunities for the local workforce, it has also resulted in increased waste production, making it the highest non-household waste generator in the area.
Table 10: Number of Commercial and Industrial Establishments in Sta Rosa City from 1980-2013
An analysis of Sta Rosa City’s employment status and economic activity from 1998 to 2013 reveals a significant shift towards the services sector, particularly in commercial establishments, which have consistently dominated since 1980 Concurrently, the agricultural sector has declined due to the conversion of agricultural land into idle and built-up areas Data from 1998 to 2013 indicates a steady increase in registered and qualified job applicants, with a notable shift in the ratio of registered to applied applicants—from a gap of 40% in 1998-2005 to just 2% in 2013 This trend highlights the growth in job hiring, offers, and applicant qualifications over time.
Table 11: Intercensal Estimates of Sta Rosa City’s Employment Status in
Various Sectors of Economic Activity
Table 12: Total Number and Rates of Registered Job Applicants and Qualified Job
Rates (%) of Registered Job Applicants
Rates (%) of Qualified Job Applicants
CONCLUSION
The study aimed to evaluate the environmental and socio-economic impacts of urbanization in Sta Rosa City by examining factors such as land conversion, waste generation, air quality, population dynamics, and economic activities Over a 24-year period from 1993 to 2017, significant changes in land use were observed, with built-up areas increasing while agricultural land steadily declined These drastic shifts in land cover have adversely affected the livelihoods of those in the agricultural sector, particularly as many have had to sell their land at low prices.
Sta Rosa City's economic growth has positively impacted its residents, but this population increase has led to significant environmental challenges, particularly in waste generation, which surpassed the average per capita levels in 2015 Additionally, air quality deteriorated, with PM 2.5 levels exceeding the US EPA's annual guideline values in 2017, showing a negative correlation with wind speed To address these issues, establishing more ambient air quality monitoring stations could create an air quality index map for Sta Rosa City, allowing for better tracking of air pollutants' intensity, frequency, and sources Furthermore, conducting thorough waste sampling and classification will provide an updated assessment of the city's total waste generation and its origins.
Promoting awareness and encouraging households and commercial establishments to engage in urban gardening and composting can significantly reduce biodegradable waste, which was the highest waste category generated in 2015 This initiative not only addresses food security but also mitigates the negative impacts of urbanization on air quality and public health An example of this success is seen in Brgy Market Area, where an “Eco-waste Center” has effectively transformed biodegradable waste into organic fertilizers Other barangays can implement similar programs to achieve these benefits.
Urbanization significantly alters land attributes and uses, often in a rapid and uncontrolled manner This study highlights that air pollution and rising waste generation are intensified by population growth and the high demand for land conversion, affecting the primary livelihoods of the population based on their environment Understanding these conditions is crucial for implementing effective solutions that balance environmental sustainability with the needs of the community.
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Appendix 1: Definition of Terms à𝒈/𝑵𝒎 𝟑 – Micrograms per cubic meter
Ambient air quality, as defined by RA 8749, refers to the overall level of pollution in a wide area, highlighting the average cleanliness of the atmosphere This concept differs from emissions measurements obtained directly at pollution sources.
Brgy /Barangay - refers the smallest administrative unit in municipality or city headed by a barangay captain
The Philippines categorizes its cities into three classes: highly urbanized cities, independent component cities that operate autonomously from their provinces, and component cities that are integrated within their respective provinces and fall under provincial administrative supervision (PSA, 2013).
Environmental degradation diminishes the environment's ability to fulfill social and ecological needs, leading to increased natural hazards and heightened community vulnerability Key contributors to this issue include pollution of land, water, and air, improper land use, and deforestation (WHO, 2008).
Land cover – refers to the physical attributes in a land such as open land, forests, and water (NOAA, 2018)
Land use – refers to the activities that are being done on the physical attributes of land or how humans use the land (NOAA, 2018)
The Landsat Program, a collaboration between NASA and USGS, consists of a series of satellite missions that monitor Earth's surface It provides the longest continuous collection of moderate-resolution remote sensing data for land use, making it a vital resource for environmental analysis and management.
Municipality refers to a city or town that has corporate status and local government
Socio-economic – refers to social related economic factors which relate to and influence one another (pdhpe.net, 2015)
Solid Waste – according to the City’s Environmental Code (City Ordinance No.1720-
All types of discarded materials, including household, commercial, institutional, and industrial wastes, as well as street sweepings, construction debris, and agricultural wastes, fall under the category of non-hazardous and non-toxic solid wastes.