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Monitoring of hourly carbon dioxide concentration under different land use types in arid ecosystem

  • Khalid Guma Biro Turk EMAIL logo , Abdulrahman O. Alghannam and Faisal Ibrahim Zeineldin
Published/Copyright: December 31, 2022
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Open Life Sciences
From the journal Open Life Sciences Volume 18 Issue 1

Abstract

Air pollution is a major factor affecting human life and living quality in arid and semiarid regions. This study was conducted in the Al-Ahsa district in the Eastern part of Saudi Arabia to measure carbon dioxide (CO2) concentration over different land-use types. Initially, the study’s land use/land cover (LULC) was classified using the spectral characteristics of Landsat-8 data. Then, sensors were placed in five sites of different LULC types to detect CO2, air temperature, and relative humidity. The Friedman test was used to compare CO2 concentration among the five sites. Five LULC types were identified over the study area: date palm, cropland, bare land, urban land, and water. The results indicated that CO2 concentration showed a maximum mean value of 577 ppm recorded from a site dominated by urban lands. During the peak time of human transportation, a maximum value of 659 ppm was detected. The CO2 concentration mean values detected for the other LULC types showed 535, 515, and 484 ppm for the bare land, cropland, and date palm, respectively. This study’s sensors and procedures helped provide information over relatively small areas. However, modelling CO2 fluctuations with time for LULC changes might improve management and sustainability.

Keywords: air pollution; carbon dioxide; land use/land cover

1 Introduction

Air quality, air-condition, and climate are major factors affecting human life and living quality [1]. The atmospheric carbon dioxide (CO2) resulting from photosynthesis plays a major role in vegetation growth and is a crucial greenhouse gas (GHG) that contributes to global warming [2]. The carbon balance of arid regions can be significantly affected by environmental stresses and human activities [3,4]. The trend of the world population is to inhabit cities by 2050 [5], which might reflect the importance of decreasing GHG emissions [6]. As a result, the global mean concentration of CO2 rose steadily from approximately 280 ppm to a level exceeding 400 ppm in the present [7].

Land use/land cover (LULC) changes due to urban expansion are considered crucial factors affecting CO2 emissions [8]. LULC change affects the climate through changes in CO2 fluxes between the land and the atmosphere [9] and accounting for approximately 10–15% of the atmospheric increase in CO2 concentrations [10,11]. However, the LULC change can modulate the land–atmosphere CO2 flux at a regional scale compared to the effects of GHG [12]. The direct and indirect GHG emissions from land-use activities such as livestock farming, manure management, fertiliser use, and paddy rice contribute around 12% of today’s total GHG emissions and about 10% rise in CO2 emissions from deforestation [13,14]. Nevertheless, terrestrial ecosystems can play an important role in sequestering atmospheric CO2 for mitigating the GHG effect [15,16].

The observation of the land surface temperature (LST) in Shenyang, China, indicated that it was higher in urban and bare lands compared to agricultural and green lands [17]. The impact of the local climate zones (LCZ) on LST was investigated in Shenyang city, China’s urban and rural areas, by Zhao et al. [18]. Their findings showed that the LST of LCZs did not follow a fixed order, as they changed from one month to another depending on the land-use type in the urban and rural areas. Therefore, monitoring CO2 concentration in urban areas is essential to measure the CO2 emission from cities and estimate its contribution to the regional carbon budget [19]. CO2 fluxes in urban areas are affected by the vegetative cover, human activity, and climate factors such as precipitation and temperature [20,21]. However, it is crucial to separate the influences of climatic factors for air pollution and environment management controlling over different land-use systems [22]. In cities, the CO2 fluxes are controlled by fuel combustion from vehicles, industries, and buildings rather than biological processes [23,24]. Vegetation cover in urban areas can significantly influence the daily and seasonal patterns of the CO2 balances [25].

Climate change and LULC change significantly affect CO2 dynamics, and the main drivers are temperature and precipitation [26]. For example, the regional drought due to climate change in the lower Mississippi River Valley affected vegetation productivity, net carbon exchange flux, and atmospheric CO2 concentration. Thus, the productivity in the drought area decreases by 23% [27]. The intervention of climate change and LULC changes and their impacts on the CO2 process were investigated at a global scale using various climate change models; accordingly, for further reading, interested readers are advised to use the following cited literature [11,28–33].

Monitoring CO2 levels is an important research theme in most parts of the world [34]. Therefore, a wide range of CO2 sensors was developed using different materials for monitoring CO2 concentration. A semiconductor sensor was used for detecting CO2 for environmental observations [35]. Also, CO2 sensors made of solid electrolytes [36–39], optic fibres [39], laser diodes [7], and non-dispersive infrared (NDIR) [40,41] detectors were utilised for observing CO2 emissions.

The atmospheric measurement of the CO2 concentration depends widely on NDIR sensors because they are stable and robust against interference by other air components, including pollutants [19]. In addition, the NDIR has excellent durability, which makes it the most popular sensor for measuring atmospheric CO2 [41]. The calibrated NDIR sensors can reasonably provide accurate air CO2 concentrations [7].

In Saudi Arabia, the increasing population within the urban and sub-urban areas puts pressure on natural resources and increases the hazard of CO2 emissions. The study area’s land-use system, located in the Eastern part of Saudi Arabia, showed a significant increase in urban lands during the last three decades [42]. Estimating CO2 emissions from the LULC changes is considered uncertain due to the difficulty of assessing this flux from measurements [43]. However, in this study, direct measurements of CO2 were made to investigate its flux along with the different LULC systems. Hence, the emergence of different patterns of land-use systems in the region will inevitably affect the amounts of CO2 emitted. Also, the land-use system usually effectively represents the spatial distribution of CO2 emissions and carbon sinks [44]. These conditions necessitate the importance of CO2 observation under different arid lands and zones. Therefore, the main objective of this study was to measure CO2 concentration over different land-use types. Also, an attempt was made to analyse the impact of CO2 emissions on the arid ecosystem of the study area.

2 Materials and methods

2.1 Study area

The study was carried out in five sites in the Al-Ahsa District located in the Eastern part of Saudi Arabia (Figure 1). The area has a population of 1.1 million (https://www.stats.gov.sa/) and is dominated by a hot, desert climate [45]. The seasonal average temperatures might reach 45°C in summer and 5°C in winter. The rainfall is present only in winter, with less than 250 mm per annum [46]. The land-use system in the study area is predominated by agricultural activities that include date palm plantation and cropping of rice and vegetables. The study sites have different land-use types, areas, and elevations, although they are located in the same climatic zone (Table 1).

Figure 1 Location of the study sites within Al-Ahsa Zone. The background of the study sites is a Landsat-8 image acquired on 17 July 2014.
Figure 1

Location of the study sites within Al-Ahsa Zone. The background of the study sites is a Landsat-8 image acquired on 17 July 2014.

Table 1

General characteristics of the five study sites and sampling time

Site Latitude Longitude Area (km2) Elevation (m) Start day and time End day and time Total hours (days)
Al-Oyon 25.61000 49.55700 13 112 01 July 2014 06:00:00 AM 30 July 2014 23:00:00 PM 714 (30)
Al-Wozyah 25.52900 49.55700 10 130 01 July 2014 06:00:00 AM 30 July 2014 23:00:00 PM 714 (30)
Al-Battaliyah 25.43000 49.63600 9 140 01 July 2014 06:00:00 AM 30 July 2014 23:00:00 PM 714 (30)
Al-Hofuf 25.32900 49.59800 192 155 01 July 2014 06:00:00 AM 30 July 2014 23:00:00 PM 714 (30)
Al-Taraf 25.35700 49.71700 8 125 01 July 2014 06:00:00 AM 30 July 2014 23:00:00 PM 714 (30)

2.2 LULC classification

The LULC classification of the study area was conducted using the Landsat-8 image (path/row is 164/042) acquired on 17 July 2014 (Figure 1) from the United States Geological Survey (https://earthexplorer.usgs.gov/). The Landsat-8 image obtained corresponds with the CO2 and climate data collected during the summer season of 2014. The image characteristics are shown in Table 2.

Table 2

Characteristics of Landsat-8 data used in this study

Sensor Bands type Wavelength (µm) Spatial resolution (m)
Operational Land Imager and Thermal Infrared Sensor Band 1, Coastal aerosol 0.43–0.45 30
Band 2, Blue 0.45–0.51 30
Band 3, Green 0.53–0.59 30
Band 4, Red 0.64–0.67 30
Band 5, Near infrared 0.85–0.88 30
Band 6, Short-wave infrared 1 1.57–1.65 30
Band 6, Short-wave infrared 2 2.11–2.29 30
Band 8, Panchromatic 0.50–0.68 15
Band 9, Cirrus 1.36–1.38 30
Band 10, Thermal infrared 1 10.60–11.19 100
Band 10, Thermal infrared 2 11.50–12.51 100
Dataset attribute Attribute value
Land Cloud Cover 0.00
Scene Cloud Cover L1 0.00
Geometric RMSE Model 7.278
Geometric RMSE Model X 4.239
Geometric RMSE Model Y 5.916

In order to identify the LULC within the study area, a field survey was conducted using a Global Positioning System instrument to obtain accurate location point data for each LULC class included in the classification process. The total number of the reference ground control points (GCPs) collected during the field survey was 110. The supervised maximum likelihood classification (MLC) technique was applied to classify the image. MLC is valid and widely used in remote sensing for image classification [47–49]. For image accuracy assessment, 30% of the collected GCPs was used to validate the classification results. In addition, visual interpretation of the unclassified satellite image, Google Earth maps, and field observations were used to verify the LULC maps. The stratified random sampling method was adopted during the image classification to reduce bias [50]. The overall accuracy, user’s and producer’s accuracy, the Kappa statistic, and conditional Kappa were derived from the classification error matrices [51].

2.3 CO2 and climate parameter measurements

The commercial GE Telaire 7001 carbon dioxide sensor measured CO2 and temperature. The GE Telaire 7001 sensor was operating using the dual-beam NDIR technology, and it was connected to an analogue input on a HOBO H22 data logger. The sensor specifications are shown in Table 3, and it has independent CO2 and temperature readings. The sensor has been factory calibrated and should be recalibrated once every 12 months using either a zero concentration gas or a gas with a specified concentration of CO2 [52]. The sensors were distributed along with the study sites based on the area covered by each side. Accordingly, four sensors were fixed at each site in Al-Oyon, Al-Wozyah, Al-Battaliyah, and Al-Taraf, and a total number of 16 sensors were fixed in Al-Hofuf (Figure 1). The temperature (T) and relative humidity (RH) sensors were distributed following the same order as the CO2 sensors. The sensors were distributed along the study site to assure data accuracy and fairness. In addition, the average sensor data from each site read the hourly CO2, T and RH were collected to represent each site fairly. The wind speed data were collected during July 2014 from a local meteorological station in the study area.

Table 3

Specifications of the GE Telaire 7001 CO2 and temperature monitor

Standards CO2 channel Temperature channel
Measurement range 0–10,000 ppm display 0–50°C display
Display resolution ±1 ppm 0.1°C
Accuracy ±50 ppm or ±5% for reading up to 5,000 ppm ±1°C
Response time 60 s for 90% of step change 20–30 min
Sample method Diffusion or flow through (50–100 mL/min)
Power requirement 100 mA peak, 20 mA average from 6 V
Operating conditions 0–50°C, 0–95% RH, non-condensing

2.4 Data analysis

The Friedman test was used to compare CO2 concentration in the five sites. The Friedman test is a non-parametric analysis used to test the significance for more than two groups; it tests the null hypothesis [53]. Thus, Friedman’s test determines whether the rank totals for each treatment differ significantly from the values expected by chance [54]. The computational formula for the Friedman test [54,55] is

(1) X r 2 = 12 nK ( K + 1 ) ∑ j = 1 k R j 2 − 3 nK ( K + 1 ) ,

where K is the number of groups (treatments), n is the number of subjects, and R j is the sum of the ranked scores in each treatment. Numbers 12 and 3 are constants, not dependent on the number of subjects or experimental conditions. The test statistic X r 2 is distributed according to the normal X r 2 distribution with K–1 degrees of freedom when the rankings are random. As n and K increase, the approximation to the X r 2 distribution improves.

Moreover, Kendall’s W test was used to assess the agreement trend among the treatments. Kendall’s W is referred to the normalization of the Friedman statistic, and it ranges from 0 to 1. The value “1” refers to the complete agreement between the raters, and “0” refers to the non-complete agreement between raters [56]. Therefore, Kendall’s W can be calculated from Friedman’s X r 2 as follows [57]:

(2) Ŵ kendall = X r 2 ( K − 1 ) .

The Inverse Distance Weighted (IDW) tool of Geostatistical Analyst in the ArcGIS 10.2 software was used to perform data interpolation for the recorded CO2 and produce the spatial distribution map [58].

The Statistica [59] and Microsoft Excel 2010 [60] software packages were used to perform the statistical analysis and produce graphs.

3 Results and discussion

3.1 LULC analysis

The study area analysis indicated that the existing LULC classes were the date palm, cropland, bare land, urban land, and water (Figure 2). However, within the study sites, the urban land occupied large areas in Al-Hofuf compared to the other sites (Table 4). The date palm orchards were dominant in Al-Battaliyah concerning its relative total occupied area. Bare lands cover about 125 km2 in Al-Hofuf, 7 km2 for Al-Oyon and Al-Wozyah, and 1.4–3 km2 in Al-Battaliyah and Al-Taraf (Table 4). The cropland areas ranged between 1 and 2 km2 in all study sites and extended to 12 km2 in Al-Hofuf. The LULC map of the study area showed how the different LULC classes were spatially distributed along with the study sites. Thus the LULC types were considered to affect the level of the CO2 concentration in a varying way depending on the human activities at each site. Al-Hofuf represents one of the two largest city centres in the Al-Ahsa District. Therefore, urban lands extend in Al-Hofuf, covering large areas. In Saudi Arabia, many residents who lived in the major cities increased from 58% in 1975 to 82% in 2014 [61]. Most urban residents migrated to the cities to seek a modern lifestyle, better employment, and educational opportunities [62]. Also, the built-up area increased by 28.9% during 1990–2014 in Dammam, the capital of the Eastern Region in Saudi Arabia [48].

Figure 2 LULC map of the study area on 02 August 2014.
Figure 2

LULC map of the study area on 02 August 2014.

Table 4

Areas of LULC for the different study sites

LULC Area (km2)
Al-Oyon Al-Wozyah Al-Battaliyah Al-Hofuf Al-Taraf
Date palm 1.0 1.4 5.4 19.0 2.0
Cropland 1.0 1.2 2.0 12.0 1.0
Urban land 4.0 0.4 0.2 36.0 2.0
Bare land 7.0 7.0 1.4 125.0 3.0
Total area (km 2 ) 13 10 9 192 8

The overall accuracy of the classified LULC map was 90%, with a Kappa statistic of 86% (Table 4). However, the user’s and producer’s accuracies of different LULC classes ranged between 81 and 100%. Also, conditional Kappa coefficients for the different LULC types are shown in Table 5. The user’s accuracy is the probability that a value predicted in a specific class is that class. That means it shows the reality on the ground. In statistical terms, the user’s accuracy measures errors of commission. The producer’s accuracy indicates the proportion of the reference data that are classified correctly for a given class. It corresponds to the statistical concept of errors of omission [49]. Urban lands show low conditional Kappa, user’s and producer’s accuracies compared to the other LULC classes. This can be attributed to the misclassification of some urban land into bare land and cropland. On the other hand, bare lands and water recorded higher accuracies because they have less mixture than the other classes.

Table 5

Accuracy assessment of LULC classification

LULC Classification accuracy (%)
User’s Producer’s Conditional Kappa
Date palm 92 91 89
Cropland 81 83 78
Bare land 94 93 89
Urban land 79 81 76
Water 100 100 100
Overall accuracy (%) 90
Kappa statistic (%) 86

The hourly recorded T patterns are almost similar in all study sites showing a minimum value of 24°C and a maximum value of 52°C (Figure 3). However, the average T values ranged between 36 and 39°C; hence sites with large vegetation covers showed relatively low T compared to sites dominated by urban and bare lands. The variation of RH along the study sites is clearly shown in Figure 4. The minimum detected RH was 24%, while the maximum was 82%. However, the mean values ranged between 29 and 36%. The low amount of RH can be found during the early morning hours, while the high values occur during the day and most of the evening. Nevertheless, the highest RH value was observed in the site dominated by vegetation cover. The average daily wind speed over the study sites showed a minimum value of 3 km/day and a maximum value of 15 km/day (Figure 5). The dominant wind direction was north to north-west. High wind speed resulted in increasing T in urban lands, while the low wind speed decreased the RH in the sites with high vegetation cover.

Figure 3 Hourly time series for the T levels in the different study sites during July 2014.
Figure 3

Hourly time series for the T levels in the different study sites during July 2014.

Figure 4 Hourly time series for the RH in the different study sites during July 2014.
Figure 4

Hourly time series for the RH in the different study sites during July 2014.

Figure 5 Average daily wind speed over the study area during July 2014.
Figure 5

Average daily wind speed over the study area during July 2014.

In urban areas, the physical environmental parameters, including climatic factors, influence pollutant dispersion, which might increase heat island [63]. Therefore, considering the meteorological conditions is important when developing policies to control urban air quality [64].

3.2 CO2 concentration and levels

The spatial distribution of CO2 over the different study sites showed high concentration levels in Al-Hofuf and Al-Wozyah, followed by Al-Battaliyah and Al-Taraf. In contrast, low levels were detected in Al-Oyon (Figure 6). The CO2 maximum mean value (μ^) of 577 ppm was observed in Al-Hofuf, while a minimum mean value of 391 ppm was detected in Al-Oyon (Figure 7). However, the statistical analysis of the CO2 levels indicated significant differences (P < 0.001) among the study sites based on the Friedman test. Nevertheless, no significant difference (P < 0.001) was observed between Al-Battaliyah and Al-Taraf (Table 6). The low value of the Ŵ kendall test (0.2) indicates that the levels of the CO2 differed across the study sites (Figure 7). The hourly time series of the CO2 levels were consistent with the spatial distribution of the CO2 concentration for the different sites (Figure 8). The time series identified high values of 500–659 ppm and low 356–450 ppm for the CO2 concentration among the study sites. In addition, the fluctuations of CO2 concentration showed high levels during the peak time of transportation at 7:00–9:00 AM, 1:00–3:00 PM, and 7:00–9:00 PM.

Figure 6 The spatial distribution of CO2 levels over the study sites.
Figure 6

The spatial distribution of CO2 levels over the study sites.

Figure 7 Comparison of the CO2 level mean values in the different study sites.
Figure 7

Comparison of the CO2 level mean values in the different study sites.

Table 6

Pairwise comparison of CO2 levels between the different study sites

Friedman coefficient (X 2) = 2,732
Degree of freedom (df) = 4
Probability (P) < 0.001
Sites pair Statistic P
Al-Oyon–Al-Battaliyah 90.14* <0.001
Al-Oyon–Al-Taraf 83.22* <0.001
Al-Oyon–Al-Wozyah 174.73* <0.001
Al-Oyon–Al-Hofuf 229.36* <0.001
Al-Battaliyah–Al-Taraf 6.91ns <0.001
Al-Battaliyah–Al-Wozyah 84.60* <0.001
Al-Battaliyah–Al-Hofuf 139.23* <0.001
Al-Taraf–Al-Wozyah 91.51* <0.001
Al-Taraf–Al-Hofuf 146.14* <0.001
Al-Wozyah–Al-Hofuf 54.63* <0.001

* = Significant; ns = not significant.

Figure 8 Hourly time series for the CO2 concentration in the different study sites.
Figure 8

Hourly time series for the CO2 concentration in the different study sites.

The high concentration levels of the CO2 in Al-Hofuf can be attributed to large urban land domination compared to the other sites (Figure 2). The fuel combustion from the cars in Al-Hofuf and the absence of public transport make the situation worse due to the increasing number of private vehicles. In the urban cities of Saudi Arabia, about 92% of the populations depend on private transportation. However, only 32% of these cities are accessible and linked to public transport systems [65]. Also, the energy use in Al-Hofuf is high compared to the suburban areas of the Al-Ahsa district due to the increase in the human population. Therefore, urban areas act as the primary source of CO2 to the ambient air [66]. However, the ambient CO2 levels could affect the air quality by involving isoprene emission in the vegetated areas [67,68]. Bare lands were the dominant land-use system in Al-Hofuf; therefore, during the summertime in July, this condition might increase the rate of CO2 emissions due to the high-temperature degrees. Similar conditions of the vast bare lands can be observed in Al-Wozyah, which also showed high CO2 concentrations. Unlike Al-Hofuf, the Al-Oyon site covered large areas of urban and bare lands, but the concentration of CO2 in it was low. This is due to the less transportation movement in Al-Oyon, as it is considered a suburban area. Also, palm orchards and farms located in the southeast of Al-Oyon may contribute to reducing CO2 emissions. Urbanisation in arid and semiarid regions can significantly impact CO2 concentrations and emissions estimated for different LULC types [69,70]. The relatively low CO2 concentration in Al-Battaliyah and Al-Taraf might be due to the extensive vegetation covers extended in these sites. Urban vegetation can play a vital role in exchanging CO2 concentrations in cities due to plant photosynthesis [40]. Also, urban vegetation is a beneficial planning strategy to control the heat island and improve the energy efficiency of buildings in urban cities [71].

The spatial distribution of CO2 concentration in the study sites shows that its levels vary according to the areas occupied by the different LULC types (Figure 9). In Al-Oyon, the low values of CO2 concentration ranging from 377 to 382 ppm occurred in cropland and date palm land-use systems, while the high 386–392 ppm was associated with the bare and urban lands (Figure 9a). Most of the area in Al-Wozyah is covered by bare lands, which shows CO2 levels of 559–561 ppm compared to 570 ppm detected over the small area occupied by the urban land (Figure 9b). The high CO2 levels resulting from the bare and urban lands in Al-Wozyah have affected the amount of CO2 concentration that appeared over the date palm and cropland areas showing CO2 levels around 546 ppm. In Al-Battaliyah, the date palm was the dominant LULC system showing CO2 concentration levels of 446–452 ppm, followed by the cropland with 440–446 ppm, bare land with 453–458 ppm, and finally urban land with 458–464 ppm (Figure 9c). The domination of bare lands in Al-Hofuf resulted in CO2 levels of 560–576 ppm, while the vast areas occupied by the urban lands raised the CO2 levels to 580–594 ppm (Figure 9d). The CO2 concentration levels in Al-Taraf ranged from 441 to 445 ppm for the date palm and cropland systems, while it ranged between 447 and 449 ppm for the bare land and almost around 450 ppm for the urban land (Figure 9e).

Figure 9 The spatial distribution of CO2 concentration over the different LULC types. (a) Al-Oyon, (b) Al-Wozyah, (c) Al-Battaliyah, (d) Al-Hofuf and (e) Al-Taraf.
Figure 9

The spatial distribution of CO2 concentration over the different LULC types. (a) Al-Oyon, (b) Al-Wozyah, (c) Al-Battaliyah, (d) Al-Hofuf and (e) Al-Taraf.

The CO2 concentration mean values detected for the different LULC types in the study area showed it was 560, 535, 515, and 484 ppm for the urban land, bare land, cropland, and date palm, respectively. Thus, the increasing and decreasing of the CO2 levels across the different LULC types over the study sites (Figure 10) are mainly attributed to the movement of active winds during the summer, where its speed ranged between 5 and 8 km/h in the north and north-west direction of the study area [72]. Moreover, the urban atmospheric CO2 concentrations variation is expected in the Middle East region. For instance, in the Gaza City of Palestine, this variation ranged between 300 and 900 ppm showing higher levels during working days than at the weekend [73]. Therefore, vegetated areas can act as mitigation measure that reduce CO2 emissions and lower the air temperature depending on the wind speed and direction from one site to another. Nevertheless, Pataki et al. [74] have indicated the limited role of urban trees in reducing GHG emissions and pollution over vast areas and environmental conditions. However, urban trees are more useful for climate and pollution adaptation approaches than emission reduction. This is because of the limitation in spaces that constrain tree canopies compared to the amount of emissions.

Figure 10 The patterns of CO2 for the different LULC types within the study sites.
Figure 10

The patterns of CO2 for the different LULC types within the study sites.

Agriculture and land-use change contribute to about 21% of the global GHG emission. Therefore, to achieve low CO2 levels, several measures can be applied. These include re-prioritising land-use systems in urban and rural areas, controlled irrigation, crop diversification, and cover crops [75]. However, land use draws attention to be used as a policy tool for carbon reduction and low-carbon planning [76]. This is because it has changed the Earth’s carbon cycle by influencing the natural carbon sources and sinks like cropland, grassland, and forests [77,78].

Vegetation has crucial environmental functions in urban areas since it removes pollutants [79]. Also, vegetation cover helps in reducing energy consumption in urban areas and hence improves their climate [80]. Therefore, appearing vegetation cover in the urban environment is essential for heat island mitigation in cities [81]. However, CO2 was the major driving factor for vegetation cover changes [82]. Moreover, the study area faces a lack of precipitation, which might be one of the main causes of CO2 concentration increase [83]. The relationship between urban form and CO2 emissions indicated that less complex cities have lower CO2 emissions, but dense cities have greater per capita CO2 emissions [84]. Also, socioeconomic factors of industrial structure, population density, and economic development level were the main drivers of CO2 emissions in urban form areas [85].

4 Conclusions

The world faces a continuous increase in atmospheric CO2 concentrations due to climate change and the consequences of land-use change. Therefore, continuous monitoring of CO2 concentrations in the air at regional and local scales will help drive global efforts to reduce CO2 emissions. In addition, site monitoring of CO2 will provide the local policymakers with the needed information to formulate policies and strategies that cut down the levels of CO2 in the atmosphere.

In this study, the CO2 concentrations in the air were monitored for 1 month during the summertime in July. The tested sites covered different LULC types of urban and suburban areas. The hourly recorded data of the CO2 showed significant variations between the study sites. For example, a maximum mean value of 577 ppm was detected in a site dominated by urban lands. However, the hourly recorded CO2 concentrations showed a maximum value of 659 ppm during the peak time of human transportation and movement. Nevertheless, the patterns of the CO2 levels showed significant variations across the different LULC types over the study sites.

The sensors and methodology used in this study provided valuable information about the CO2 concentrations and levels over a specific site. However, there is a real challenge in designing a sensor network capable of continuously detecting CO2 in large open space areas over a long time. In addition, modelling CO2 variation over time due to the LULC changes might help predict the best practices for land-use management that can reduce the CO2 levels in the atmosphere. Therefore, adequate management of agricultural lands and reducing CO2 emissions and pollution from crop and animal production will help preserve and enhance the air quality for most LULC types dominated in the study area.

Acknowledgments

The authors would like to thank the anonymous reviewers for their contribution to the improvement of the manuscript. Also, we would like to express our gratitude to Dr. Yousef Ahmed Alkhamis, the director of the Water Studies Center at King Faisal University, for his continuous support.

  1. Funding information: This work was supported by the Deanship of Scientific Research, King Faisal University, Saudi Arabia (grant number NA000251).

  2. Author contributions: A.O.A. set the concept and the methodology; A.O.A., K.B.T., and F.I.Z. carried out field data collection; K.B.T. performed software and formal analysis of the data, and writing the original draft of the manuscript; F.I.Z. did the review writing and editing of the manuscript and the work supervision. All authors have read and agreed to the published version of the manuscript.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2022-08-30
Revised: 2022-10-15
Accepted: 2022-11-13
Published Online: 2022-12-31

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
https://doi.org/10.1515/biol-2022-0534
Keywords for this article
air pollution; carbon dioxide; land use/land cover
Creative Commons
BY 4.0

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  168. Clinicopathological characteristics of co-existing or mixed colorectal cancer and neuroendocrine tumor: Report of five cases
  169. Role of menopausal hormone therapy in the prevention of postmenopausal osteoporosis
  170. Precisional detection of lymph node metastasis using tFCM in colorectal cancer
  171. Advances in diagnosis and treatment of perimenopausal syndrome
  172. A study of forensic genetics: ITO index distribution and kinship judgment between two individuals
  173. Acute lupus pneumonitis resembling miliary tuberculosis: A case-based review
  174. Plasma levels of CD36 and glutathione as biomarkers for ruptured intracranial aneurysm
  175. Fractalkine modulates pulmonary angiogenesis and tube formation by modulating CX3CR1 and growth factors in PVECs
  176. Novel risk prediction models for deep vein thrombosis after thoracotomy and thoracoscopic lung cancer resections, involving coagulation and immune function
  177. Exploring the diagnostic markers of essential tremor: A study based on machine learning algorithms
  178. Evaluation of effects of small-incision approach treatment on proximal tibia fracture by deep learning algorithm-based magnetic resonance imaging
  179. An online diagnosis method for cancer lesions based on intelligent imaging analysis
  180. Medical imaging in rheumatoid arthritis: A review on deep learning approach
  181. Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach
  182. Utility of neutrophil–lymphocyte ratio and platelet–lymphocyte ratio in predicting acute-on-chronic liver failure survival
  183. A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia
  184. TNF-α and IL-8 levels are positively correlated with hypobaric hypoxic pulmonary hypertension and pulmonary vascular remodeling in rats
  185. Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
  186. Comparison of the prognostic value of four different critical illness scores in patients with sepsis-induced coagulopathy
  187. Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development
  188. Hepatobiliary surgery based on intelligent image segmentation technology
  189. Value of brain injury-related indicators based on neural network in the diagnosis of neonatal hypoxic-ischemic encephalopathy
  190. Analysis of early diagnosis methods for asymmetric dementia in brain MR images based on genetic medical technology
  191. Early diagnosis for the onset of peri-implantitis based on artificial neural network
  192. Clinical significance of the detection of serum IgG4 and IgG4/IgG ratio in patients with thyroid-associated ophthalmopathy
  193. Forecast of pain degree of lumbar disc herniation based on back propagation neural network
  194. SPA-UNet: A liver tumor segmentation network based on fused multi-scale features
  195. Systematic evaluation of clinical efficacy of CYP1B1 gene polymorphism in EGFR mutant non-small cell lung cancer observed by medical image
  196. Rehabilitation effect of intelligent rehabilitation training system on hemiplegic limb spasms after stroke
  197. A novel approach for minimising anti-aliasing effects in EEG data acquisition
  198. ErbB4 promotes M2 activation of macrophages in idiopathic pulmonary fibrosis
  199. Clinical role of CYP1B1 gene polymorphism in prediction of postoperative chemotherapy efficacy in NSCLC based on individualized health model
  200. Lung nodule segmentation via semi-residual multi-resolution neural networks
  201. Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI
  202. A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis
  203. Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer’s disease
  204. Effectiveness of the treatment of depression associated with cancer and neuroimaging changes in depression-related brain regions in patients treated with the mediator-deuterium acupuncture method
  205. Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis
  206. Monitoring and evaluation of anesthesia depth status data based on neuroscience
  207. Exploring the conformational dynamics and thermodynamics of EGFR S768I and G719X + S768I mutations in non-small cell lung cancer: An in silico approaches
  208. Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer
  209. Incidence of different pressure patterns of spinal cerebellar ataxia and analysis of imaging and genetic diagnosis
  210. Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model
  211. Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders
  212. From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology
  213. Ecology and Environmental Science
  214. Monitoring of hourly carbon dioxide concentration under different land use types in arid ecosystem
  215. Comparing the differences of prokaryotic microbial community between pit walls and bottom from Chinese liquor revealed by 16S rRNA gene sequencing
  216. Effects of cadmium stress on fruits germination and growth of two herbage species
  217. Bamboo charcoal affects soil properties and bacterial community in tea plantations
  218. Optimization of biogas potential using kinetic models, response surface methodology, and instrumental evidence for biodegradation of tannery fleshings during anaerobic digestion
  219. Understory vegetation diversity patterns of Platycladus orientalis and Pinus elliottii communities in Central and Southern China
  220. Studies on macrofungi diversity and discovery of new species of Abortiporus from Baotianman World Biosphere Reserve
  221. Food Science
  222. Effect of berrycactus fruit (Myrtillocactus geometrizans) on glutamate, glutamine, and GABA levels in the frontal cortex of rats fed with a high-fat diet
  223. Guesstimate of thymoquinone diversity in Nigella sativa L. genotypes and elite varieties collected from Indian states using HPTLC technique
  224. Analysis of bacterial community structure of Fuzhuan tea with different processing techniques
  225. Untargeted metabolomics reveals sour jujube kernel benefiting the nutritional value and flavor of Morchella esculenta
  226. Mycobiota in Slovak wine grapes: A case study from the small Carpathians wine region
  227. Elemental analysis of Fadogia ancylantha leaves used as a nutraceutical in Mashonaland West Province, Zimbabwe
  228. Microbiological transglutaminase: Biotechnological application in the food industry
  229. Influence of solvent-free extraction of fish oil from catfish (Clarias magur) heads using a Taguchi orthogonal array design: A qualitative and quantitative approach
  230. Chromatographic analysis of the chemical composition and anticancer activities of Curcuma longa extract cultivated in Palestine
  231. The potential for the use of leghemoglobin and plant ferritin as sources of iron
  232. Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM
  233. Bioengineering and Biotechnology
  234. Biocompatibility and osteointegration capability of β-TCP manufactured by stereolithography 3D printing: In vitro study
  235. Clinical characteristics and the prognosis of diabetic foot in Tibet: A single center, retrospective study
  236. Agriculture
  237. Biofertilizer and NPSB fertilizer application effects on nodulation and productivity of common bean (Phaseolus vulgaris L.) at Sodo Zuria, Southern Ethiopia
  238. On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies
  239. Exopolysaccharides from Pseudomonas tolaasii inhibit the growth of Pleurotus ostreatus mycelia
  240. A transcriptomic evaluation of the mechanism of programmed cell death of the replaceable bud in Chinese chestnut
  241. Melatonin enhances salt tolerance in sorghum by modulating photosynthetic performance, osmoregulation, antioxidant defense, and ion homeostasis
  242. Effects of plant density on alfalfa (Medicago sativa L.) seed yield in western Heilongjiang areas
  243. Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
  244. Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture
  245. Animal Sciences
  246. Effect of ketogenic diet on exercise tolerance and transcriptome of gastrocnemius in mice
  247. Combined analysis of mRNA–miRNA from testis tissue in Tibetan sheep with different FecB genotypes
  248. Isolation, identification, and drug resistance of a partially isolated bacterium from the gill of Siniperca chuatsi
  249. Tracking behavioral changes of confined sows from the first mating to the third parity
  250. The sequencing of the key genes and end products in the TLR4 signaling pathway from the kidney of Rana dybowskii exposed to Aeromonas hydrophila
  251. Development of a new candidate vaccine against piglet diarrhea caused by Escherichia coli
  252. Plant Sciences
  253. Crown and diameter structure of pure Pinus massoniana Lamb. forest in Hunan province, China
  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
  255. Tissue culture and rapid propagation technology for Gentiana rhodantha
  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
  258. Chlorate-induced molecular floral transition revealed by transcriptomes
  259. Effects of warming and drought on growth and development of soybean in Hailun region
  260. Effects of different light conditions on transient expression and biomass in Nicotiana benthamiana leaves
  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
  262. Distinguish Dianthus species or varieties based on chloroplast genomes
  263. Comparative transcriptomes reveal molecular mechanisms of apple blossoms of different tolerance genotypes to chilling injury
  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
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