Measurement of Carbon Dioxide Concentration in the Outdoor Environment

Transcription

1 R32 Project: Measurement of Carbon Dioxide Concentration in the Outdoor Environment Woo Ka Ming Physics Department, from Chinese University of Hong Kong, CONTENTS: Introduction 1. Data Analysis: 1.1 Measurements 1.2 Monthly Variation 1.3 Seasonal Variation 1.4 Diurnal Variation 1.5 CO 2 conc. frequency distribution 1.6 Trip Record 2. Dynamic Behaviors: 2.1 Wind speed 2.2 Solar radiation 2.3 Wind direction 2.4 Pressure, Relative humidity, Temperature 2.5 Stimulation Conclusion Further studies Appendix: Probability distortion method Introduction: The atmosphere carbon dioxide CO 2 is one of the major greenhouse gases that show an increasing trend globally accompanied with global warming effect of CO 2 that certainly alerts people about its importance. The rise of increasing CO 2 is attributed to the intense industrial activities like burning of fossil fuel and the increasing demand of energy, resulting in a large exhaust of CO 2 inevitably. From the past history, the atmosphere CO 2 has increased from 288 ppm before the industrial revolution to more than 350 ppm at present 1. With this gigantic rise in CO 2 concentration level and unknown consequences, it is thus worth continuous observation and monitor. Hong Kong is a dense urban city near the mouth of Pearl River delta, therefore the unique atmosphere compositions characterize the city. For instance, we found wind blows from the South China Sea contains less anthropogenic CO 2 and thus offers a good background reference for Hong Kong as compared to the polluted air with high CO 2 concentration level blown from NW 1 Quoted from paper: Continuous Monitoring of Carbon Dioxide Concentration in the Urban Atmosphere of Nagoya, direction, due to industrial emission from Pearl River delta region. Because of the rapid urbanization, population expansion and the geographical location of Hong Kong, the issue of man-and-environment is being under a deep concern to the society. Therefore, the objectives of this report aim to analyze the local pattern and the dynamic behavior of CO 2 conc. in Hong Kong. Section 1 Data Analysis 1.1 Measurements: The chosen site of measurement was the Kings Park station that is one of the well-equipped stations of Hong Kong Observatory with the study period from 17 th June to 26 th July, a total of 40 days. The sensor used for monitor of the atmospheric CO 2 was a non-dispersive IR gas analyzer LI-COR( LI-820) which works based on the measurement of the absorption of IR energy as CO 2 travels through an optical path and then compared to the difference ratio in the IR energy absorption between a reference air. The concentration obtained was a continuous real time series with 1-minute interval data

2 output. The sample air collected in Kings Park was about 100m height above the ground level with green plants surrounding the hill and the gas sensor LI-820 was supported by a stand with height about 1m above the ground. Kings Park locates at the heart of urban area near Hung Hom and because of its height, it is assumed the measured air was well mixed and in equilibrium state that provides a good reference for CO 2 concentration monitor in the urban site of Hong Kong. 1.2 Monthly Variation: conc. in June and July are about 8 ppm. Contribution of extreme weather conditions were covered during this study period, such as tropical cyclones LINFA in 20 th June, NANGKA in 26 th June, SOUDELOR in 10 th July and MOLAVE in 17 th July. The mean value of CO 2 conc. of each month can also be seen in table 1 but the study period is too short to see seasonal variation, on the other hand it is reported that CO 2 conc. usually reaches its highest value in winter and lowest in summertime due to the active sink of CO 2 caused by photosynthesis action. 1.3 Seasonal Variation: Fig. 1: Real time series of the atmospheric CO 2 concentration based on running 15-minute average over the study period. Fig. 2: Daily mean over the study period based on the 1-minute data output per day. Unit: ppm Min Max Mean S. D. June July Overall Table 1: Minimum, maximum, mean and standard deviation of the CO 2 conc. over the study period based on data in Fig. 1 During the whole study period, the maximum observed CO 2 conc. was ppm and the minimum conc. was ppm and mean conc ppm which is much higher the global mean of CO 2 conc ppm in In table 1 we see the standard deviations of CO 2 2 Data was obtained from following website: Fig. 3: Daily minimum( the blue dots) and daily maximum( the red dots ) over the study period based on the 1-minute data output per day. In Fig. 3, the variation of daily minimum of CO 2 conc. was generally smaller than the daily maximum, statistically the standard deviation of daily minimum is ppm while that for daily maximum is ppm.

3 Physically we regard the daily minimum is more stable because it corresponds to the upper limit of absorption of CO 2 by plants photosynthesis, which is one of the sinks of CO 2, so the stable daily minimum is attributed to the saturation of photosynthesis. On the other hand, the daily maximum varied in large amplitude simply because it corresponds to the exhaust of CO 2 which can be no upper bound due to human activities. 1.4 Diurnal Variation: Fig. 4: Diurnal average based on ensemble mean of the 1-minute data throughout the study period at the same time during a day and then take the running 60-min mean. Each vertical bar represent 3-hour interval. In general the photosynthesis process carried out by plants dominates the daytime that provides as a strong sink of CO 2 and its effect can be seen from the big drop in CO 2 conc. from 9 o clock to 18 o clock in Fig. 4. However the photosynthesis in turn depends on the support of solar energy, we can see from the study any change in the amount of solar radiation( caused by the change in cloud amount that block the sunlight ) at the same station does lead to almost immediately response of the CO 2 conc. The functional relation between CO 2 conc. and solar radiation will be discussed later in section 2.2, but we can simply to say the higher solar radiation value then the lower CO 2 conc. will be. At the same time it is found that wind speed also affects the CO 2 conc. directly in another mechanism: equilibrium between the ventilation of CO 2 caused by wind and the release of CO 2 from soil respiration and human activities. Their relations are strong that small wind usually facilitates the accumulation of CO 2 but strong wind on the opposite helps to remove them. Statistical result in Fig. 6 (a) shows wind speed is relative small during night that helps to account for the general high CO 2 conc. level in nighttime as compared to daytime. Up to present study, both solar radiation and wind speed are two important variables that lead to direct change of CO 2 conc. with clear mechanisms. Fig. 5: Diurnal variation over the study period based on the 60-min interval plot of Fig. 4. Each vertical bar represent 3-hour interval. The ( a ) red horizontal line was the mean mean CO 2 conc ppm Fig. 4 Fig. 5 Minimum ppm ppm at 12:00 Maximum ppm ppm at 21:00 Mean ppm ppm Table. 2: Statistics results of Fig. 4 and Fig. 5 respectively. ( b ) Fig. 6: (a) Diurnal variation of the 1-minute scalar wind speed over the study period in unit of ms -1. (b) Diurnal variation of 1-minute solar

4 radiation over the study in unit of W m -2. Both graphs are based on the ensemble mean of the 1-minute data throughout the study period at the same time during a day and then take the running 60-min mean. Finally there are two small peaks occurred at about 9 and 21 o clock which indeed coincide with the rush-hour occurs everyday and it usually accompanied with a huge CO 2 emission from vehicles. This is a characteristic of a well-developed urban city that contributes an additional CO 2 in the atmosphere. In Fig. 7 the most frequent CO 2 conc. value appears in range between ppm with 16.2 %, as compared to the mean CO 2 conc. value ppm in Fig. 5. The asymmetric distribution curve is common in statistical physics, for example it is similar to an energy density function: 1.5 CO 2 conc. frequency distribution: Fig. 8 Energy density function curve overlap with the frequency distribution curve in Fig. 7. Fig. 7: Frequency distribution of CO 2 conc. with bin size 2.5 ppm from ppm. The pink dots are the CO 2 conc. probability value based on the running 15-min mean data over the study period. CO2 Prob. CO2 Prob. CO2 Prob. CO2 Prob E Table 3: Statistics result based on Fig. 7. The probability values for those CO 2 conc., in unit of ppm, higher than 450 ppm are all zero. By analogy, we may try to explain the distribution behavior of CO 2 conc. similar to the allowed wavelength emitted from blackbody radiation, since physical solution does not allow unlimited high frequency radiation, the emission of ultra high CO 2 conc. is forbidden. The lowest threshold conc. value depends on the removal capacity of CO 2 by photosynthesis. When other CO 2 sinks present, it is expected that the lowest threshold conc. value will extend to the left hand side and similar argument for the highest threshold conc. value. 1.6 Trip Record Table 4: Trip record in 22th July, 2009 Time CO 2 conc. recorded CO 2 conc. value happen at the same time in Fig. 4 Tai Mok Shan 9: ppm ppm Hok Tsui 15: ppm ppm Wan Chai 17: ppm ppm Tsuen Wan 12: ppm ppm

5 In July 22 nd, 2009, a trip record was carried out to investigate CO 2 conc. level at different meaningful locations. Tai Mok Shan is the highest mountain in Hong Kong, we took record at highest point aside the radar station. The average CO 2 conc. recorded was ppm as expected because pressure decreases with increasing height and therefore low pressure allows gaseous expand, resulting in dilution of CO 2 conc. Hok Tsui locates at the coastal region and far from urban areas, its surrounding has little vegetation only, hence the CO 2 conc. was less being affected by human exhaust and was a good choice for long period background CO 2 conc. monitor in Hong Kong. The average record was ppm which was smaller than the average record measured in Wan Chai, ppm, and Tsuen Wan, ppm. The last two sites were the hearts of urban area: Wan Chai is a famous business district while Tsuen Wan has high housing density. All the four measured sites had their own value in reflecting their unique locations. However, the general low values of CO 2 conc. recorded were attributed to the use of Vaisala GMP343 CO 2 sensor as it always measures smaller CO 2 conc. value than that of the LI-COR Li-820. Section 2 Dynamic Behaviors 2.1 Wind Speed: wind spd Freq. wind spd Freq. wind spd Freq. wind spd Freq Table 5: Detail of the frequency plot in Fig. 9, frequency was zero for those 1-minute scalar wind speed greater than 20 ms -1. (a) (b) Fig. 10: (a) & (b) are the same graphs that CO 2 conc. plot against 1-min scalar wind speed over the study period with bin size 0.1 ms -1 from 0 to 17 ms -1. In Fig.10 (b), the sum of fraction for wind speed greater than 6 ms -1 was % ( as shown by the blue dots ). Because the diurnal variation of 1-minute scalar wind speed was less than 7 ms -1 ( as shown in Fig. 6 (a) ), we fits the scatter plot for wind speed ranging from 0 to 6 ms -1 only as shown below in Fig. 11. Fig. 9: Frequency distribution of 1-min scalar wind speed over the study period with bin size 1 ms -1 from 0 to 50 ms -1 and the maximum 1-minute scalar wind speed recorded was 16.6 ms -1.

6 Fig 11: CO 2 conc. plot against 1-min scalar wind speed from 0 to 6 ms -1 with bin size 0.1 ms -1. In Fig. 11 we see clearly the negative relation between CO 2 conc. and the wind speed which can be explained by the ventilation of CO 2 caused by wind, the larger value of wind speed then the more CO 2 can be removed from the atmosphere, resulting in dilution of atmospheric CO 2. The best fitting curve can be found is: If steady state is considered, all time derivatives become zero, and with negligible background concentrations such that: Hence the steady state equation is simple and with immediate solution: where u is CO 2 conc. in unit ppm; x is wind speed in unit ms -1, evaluated according to data in Fig. 11. The statistical R-squared between actual CO 2 conc. and the predicted value is unexpectedly as high as Other than the qualitative explanation, we employ the simplest Box Model without chemical transformation derived from consideration of mass conservation within a box with unit width normal to wind direction, length L in the direction of wind flow and height h equal to the mixing depth. Since the rate of change of mass in the box equals to the sum of the rate of change of pollutant mass, change caused by horizontal advection and entrainment from the top resulting from the growth of mixing height. When wind blows from one direction with mean wind speed value v normal to the box surface, then the differential equation can be written as: From above solution we clearly see that the mean wind speed is inversely proportional to the average CO 2 conc. in atmosphere. Of course, the above derivation is not detail enough to account for the whole physical system of the dispersion and advection motion of CO 2 as it is quite different from the fitting function we obtained. Also note that the term h in allows us to add the effect of temperature, pressure and solar radiation that cause changes in the volume of the box. 2.2 Solar Radiation

7 Fig. 12: Frequency distribution of 1-minute solar radiation during the study period. Fig. 14: Ensemble mean of CO 2 conc. plot against 1-min wind direction occurred at the same time of a day throughout the study period with bin size 30 Deg. Wind Direction CO 2 conc. ppm Wind Direction CO 2 conc. ppm Wind Direction CO 2 conc. ppm Wind Direction CO 2 conc. ppm Table 6: Detail of Fig. 14 Fig. 13: CO 2 conc. plot against 1-min solar radiation occurred at the same time of a day throughout the study period with bin size 10 Wm -2 from 0 to 1400 Wm -2. The fitting curve was the simplest with high R 2 value fitting. In Fig. 13 the fitting curve was given by: In Fig. 14, those wind comes from the sea in SE direction contains less man-made pollutants and so less frequent to have high CO 2 conc. level while those wind comes from the Pearl River delta in NW direction on the opposite does contain higher CO 2 conc. level due to serious industrial gas exhaust. 2.4 Pressure During daytime the photosynthesis action carried out by plants governs the change of the CO 2 conc. as one might imagine they are powerful sinks for the removal of the CO 2 from atmosphere. The negative relation between CO 2 and solar radiation can be seen in Fig. 13. (a) 2.3 Wind direction Carbon dioxide conc. during the study period (b)

8 Fig. 15: (a) Frequency distribution of pressure over the study period (b) CO 2 conc. plot against pressure based on the average of 1-minute data output for both CO 2 conc. and pressure occurred at the same time As shown in Fig. 16 (b), the correlation between CO 2 conc. and pressure is , weak relation might exist. throughout the study period. 2.4 Temperature (a) As shown in Fig. 15 (b), the correlation between CO 2 conc. and pressure is implying only weak relation might exist. 2.4 Relative humidity (a) (b) Fig. 17: (a) frequency distribution of temperature; (b) CO 2 conc. plot (b) against temperature based on the average of 1-minute data output for both CO 2 conc. and temperature occurred at the same time throughout the study period. Fig. 16: (a) frequency distribution of relative humidity; (b) CO 2 conc. plot against relative humidity based on the average of 1-minute data output for both CO 2 conc. and relative humidity occurred at the same time throughout the study period. In Fig. 17 (b), the correlation between CO 2 conc. and temperature is , weak relation might exist.

9 2.5 Stimulation Assume CO 2 conc. u depends on two weather variables: Both wind speed and solar radiation are well fitted with known fitting functions, however the above stimulation does not offer the whole dynamic picture of CO 2 conc. as it may depend on many other unknown weather elements absent in present study. On the other hand, the above stimulation provides a test for present findings of the relation between wind speed, solar radiation and CO 2 conc. (a) (b) Fig. 19: Stimulation result: Red curve: Stimulated diurnal variation of CO 2 cocn. over the study period based on the diurnal variation of wind speed and solar radiation shown in Fig. 18 (a) and (b). Blue curve: The actual diurnal variation based on ensemble average of 1-minute CO 2 conc. occurred at the same time during a day throughout the study period. Each vertical bar marked on Fig. 20 represents 3-hour interval. In Fig. 19, we could see the stimulated curve was: (1) in general above the actual curve before 6 o clock during a day which may suggest some unknown mechanisms present to absorb or removal CO 2 ; (2) in general below the actual curve within 6 18 o clock during a day which coincides with the same period of the appearance of solar radiation. The unexpected small value of stimulated curve during this period may suggest sunlight was frequently blocked and suppress the photosynthesis action. (3) no observable peak during rush-hour, o clock, simply because there was no source term to stimulate the man-made emission of CO 2 in the differential equation. Fig. 18: (a) Diurnal average of 1-minute wind speed in unit of ms -1 (b) Diurnal average of 1-minute solar radiation that they occurred at the same time during a day throughout the study period.

10 Conclusion: The diurnal behavior of CO 2 conc. is well studied mainly based on the strong relation between solar radiation and wind speed. However, the stimulation result does not exactly capture the diurnal pattern implying some important weather mechanisms might exist. The mean CO 2 conc. at Kings Park is about 397 ppm which is much higher than the global mean CO 2 conc. level, ppm, claimed in Further studies: 1. Conduct a longer period study to construct the scatter plot between CO 2 conc. and other weather variables and look for their best fitting functions for the stimulation use. 2. Depth study for any dissimilarity between stimulated and actual curves like Fig. 19, as it might provide some useful clues to any missing finings. 3. Cases studies for extreme weather conditions like tropical cyclone as it provides unusual low pressure, high temperature and more uni-direction wind. 4. Cases studies for the unusual high CO 2 conc. level from the daily mean plot like Fig. 2 which offers a good chance to test for our findings: low wind speed facilitates the accumulation of CO 2 ; low solar radiation suppress photosynthesis action to remove CO 2 and NW wind usually brought us high polluted air including high CO 2 conc. level. 5. Observe any relation between small fluctuation of LI-820 reading occurred in a day and weather conditions.

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