The primary objective of this study is to characterize the intra-city spatial variability of PM2.5 and ultrafine particulate matter in Dhaka city through high spatial resolution measurements. LIST OF FIGURES Page no. Figure 4.1: Comparison of PM2.5 measured by a cheap sensor with PM2.5 measured by BAM at the US Embassy, Dhaka. 31 Figure 4.2: Comparison of the spatial variability of PNC and PM2.5. a) Average PNC measured at different locations.
36 Figure 4.3: Comparison of spatial variability of PNC and PM2.5 across different site types. a) Distribution of PNC in urban background, residential, commercial and mixed housing types. 38 Figure 4.5: Comparison of mean PM2.5 measured in different background five at different times (ie morning, afternoon, evening). 40 Figure 4.6: Spatial correlation between mean PNC and PM2.5 measured at 35 locations in Dhaka city. a) Scatter plot of absolute concentrations, (b) scatter plot of log-transformed concentrations.
The COD value for each site is estimated relative to average PNC and PM2.5 measured at five background sites.
INTRODUCTION
- Background of the Research
- Study Objectives
- Scope of Research
- Organization of the Thesis
The central monitoring approach is extremely expensive and not very suitable for sampling within a city with high spatial resolution. Recently, mobile and distributed monitoring approaches have shown great promise for urban air pollution monitoring with high spatial resolution [13] [14]. In addition, emerging low-cost air monitoring sensors have been shown to hold great promise for high spatial density air monitoring.
The overall objective of this study is to characterize intra-urban spatial variation of fine and ultrafine particle concentrations in Dhaka city using high spatial density measurements. The present study is to investigate and characterize the intra-urban spatial variability of PM2.5 and UFP in Dhaka city by high-spatial resolution measurements. The primary objective of this study is to characterize intra-urban spatial variation of fine (PM2.5) and ultrafine particulate matter (UFP) in Dhaka city using spatial density measurements.
The primary focus of this study is to characterize the intra-city spatial variability of fine and ultrafine dust concentrations in Dhaka city using high spatial density measurements.
LITERATURE REVIEW
- General
- Difference Between Fine (PM 2.5 ) and Ultrafine Particles (UFP)
- Health Impacts of UFPs and PM 2.5
- Major Sources of UFP and PM 2.5
- Spatial variability of UFP and PM exposures
- Intra-urban Spatial Variability of UFP and PM 2.5
- Quantification of intra-urban spatial variability
- Relevant studies in Bangladesh
- Gaps in Knowledge
- Summary
Furthermore, the characterization of intra-urban spatial variability of ultrafine particles (UFP) and PM2.5 concentrations was analyzed and their correlation. This study considers valuable insights into the extent of spatial variations to fine and ultrafine particle concentrations. The dataset generated from this study that helps quantify the spatial heterogeneity, and local source impact on fine and ultrafine particulate matter exposure in Dhaka city.
The thesis is presented in five chapters, where the most important aspects of the entire research are covered in detail. This chapter reviews background information on fine and ultrafine particles, their sources, impacts, and spatial variability.
METHODOLOGY
Introduction
First, it presents a brief description of the short-term measurement of ultrafine particles (UFPs) and PM2.5. Subsequently, a brief description of the study area and selection of measurement sites is described in detail.
Study Area
Sampling Locations
নির্বাচিত স্থানগুলো হলো মার্কিন দূতাবাস, এয়ারপোর্ট রোড, হাতিরঝিল, লালবাগ, শিমন্ত স্কয়ার, আগাশাদেক রোড, হাসপাতাল স্কয়ার রোড, সংসদ ভবন, টিএসসি (ঢাকা বিশ্ববিদ্যালয়), যমুনা ফিউচার পার্ক, মিরপুর মিল্ক ভিটা, তেজগাঁও, এলিফ্যান্ট রোড এবং অফিসার্স। ইস্কাটন ক্লাব। ) নির্বাচিত স্থানগুলো হলো রামপুরা, মহাখালী, নিউমার্কেট, গুলিস্তান, ফার্মগেট, টেকনিক্যাল, মিরপুর ১ ও মৌচাক মার্কেট।
Measurement of ultrafine particles
Measurement of PM 2.5
The measurement was carried out for PM2.5 at different parts of the day (i.e. morning, afternoon, evening).
Spatial Correlation Between PNC and PM 2.5
Quantifying special heterogeneity
Estimated coefficient of divergence (COD) values are presented to describe the spatial heterogeneity of PNC and PM2.5 levels. PM2.5 levels in urban background locations are the lowest, followed by residential and commercial locations. For example, the standard deviation (SD) of PM2.5 measured in selected urban background areas is 9 µg m-3.
Both PNC and PM2.5 concentrations are lower in urban background sites and higher in commercial sites. On the other hand, PM2.5 levels in commercial locations are about 10-20% higher than urban background levels. The PNC level is approximately three times higher than the urban background, PM2.5 is 20% higher than the urban background.
There is a modest difference between PM2.5 levels at background and residential locations (10% higher at residential locations). PM2.5 levels are significantly (about 2 times) higher in the dry season compared to the rainy season in all locations. This section quantifies the spatial heterogeneity of PNC and PM2.5 using the divergence coefficient (COD).
The COD value for each site is estimated relative to the mean PNC and PM2.5 measured at five background sites. In both seasons, the intra-urban spatial variability of PM2.5 remains within 10-20% of the urban background level.
RESULTS and DISCUSSIONS
Introduction
This chapter presents the results of the measured PM2.5 and particle number concentration (PNC) datasets, also describes data correlation and analysis.
Data quality assurance
A simple correction based on this comparison of US Embassy data is applied to all PM2.5 data collected at different locations.
Measured Spatial Variability of PNC and PM 2.5
Comparison of Spatial Variability of PNC and PM 2.5
Cities are classified according to land use attributes (urban background, residential, commercial and mixed residential and commercial. This shows the impact of larger traffic park and commercial (restaurant) emissions from cooking in commercial locations. However, the overall impact of local sources on urban PM2 levels .5 much smaller than that of PNC.
There is a relatively uniform urban background for PM2.5, which is likely to be the regional air mass. Furthermore, local sources cause intra-urban variability of PM2.5, which ranges between 10-20% of background levels.
The average PM2.5 at background sites was 97 μg/m3 in the dry/winter season against 39 μg/m3 in the rainy season. While there is a clear shift in the background level during the winter/dry season, the intra-urban spatial patterns remain similar between seasons. The regional air mass becomes more polluted in dry/winter seasons due to the influence of meteorological (e.g. low mixing height in winter) and other seasonal factors (e.g. influence of seasonal source biomass burning in brick making).
However, the intra-urban pattern most influenced by local emission remains the same. While it was planned to collect PNC data during both seasons, it did not materialize due to an instrumental failure.
Temporal Variation of measured PM 2.5 at different times
For the urban background, the morning concentration is lower than the afternoon and 15% lower than the evening concentration. For a total of 35 sites and the other 2 seasons (dry, rainy), the peak was in the afternoon or early night. The higher morning concentration for the urban background indicates that the local source influence does not have a large effect on PM2.5.
As atmospheric conditions (low wind speed and temperature inversion) dominate in the morning, air particles accumulate in the lower atmospheric layer, leading to foggy conditions in the early hours. A fair number of studies have confirmed the dependence of the temporal variation of particulate matter in response to the variability of meteorological parameters. However, this daily bimodal peak pattern has been observed for both stations – close and far from anthropogenic activities, which is indicative of the influence of factors other than anthropogenic activities on PM2.5 variability [97].
Meteorological Influence on PM 2.5 Variation
The relationship between relative humidity and PM2.5 for 35 different locations and seasons of Dhaka is analyzed and the summary is shown in Table 4.3 (b). Moderate negative correlation occurs during dry season/winter relative humidity with rainy season and dry season/winter PM2.5. Perhaps because, during the dry/winter season, there is not much relative humidity available to have such a pronounced effect on PM2.5.
Low correlation occurs during rainy season relative humidity with dry/winter and rainy season PM2.5. During the rainy season, relative humidity begins to increase, which precipitates the atmospheric particles through dry deposition. So when relative humidity is high, relatively weak correlation between relative humidity and PM2.5 is observed.
Spatial Correlation between PNC and PM 2.5
Quantification of Spatial Heterogeneity of PNC and PM 2.5
Color graph showing pairwise coefficient of divergence (COD) values for PNC and PM2.5 measured in 35 areas of Dhaka city. In most cases, the value is less than 0.2, indicating that the PM2.5 pollution levels in the measured locations are homogeneous in space. Relative to the background level, residential and mixed locations show a moderate range of spatial heterogeneity for PNC (COD commercial locations show a much higher level of spatial heterogeneity (COD.
On the other hand, relative to background levels, PM2.5 levels in residential, mixed-use, and commercial locations are mostly spatially homogeneous (COD < 0.2).
CONCLUSION AND RECOMENDATION
General
Conclusion
Lowest concentration in the morning and sub-lowest concentration in the afternoon for all areas except urban background. For a total of 35 locations and the remaining 2 seasons (dry, rainy) the peak was in the afternoon or early at night.
Limitations and Recommendations for Future Study
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