Correlation: weather parameters and PM2.5 in Hanoi and HCMC, Vietnam

4. Results and Discussion

3.1 Big pictures

The horizontal transport of PM2.5 is influenced mostly by wind speed and direction. Figs. 1-2 presented the windrose and wind speed distribution for Hanoi and HCMC. Dominant winds in Hanoi is from the North-East and South-East directions, and HCMC is South-East and South-West. The long-tail distribution in Fig. 2 indicated a stronger wind in HCMC located in the South of Vietnam and with a flatter surrounding terrain. Hanoi is located in the North in Red River delta surrounded by tall mountain ranges (over 1000m) on the North West direction.

windrose merra2
Fig. 1: Windroses for Hanoi and Ho Chi Minh city (2016-2019).
windrose dist
Fig. 2: Wind speed distribution.

PM2.5 data in CSV files are tagged with four categories as shown in Figure 3. The distribution provides a reference for the performance of a FEM station. For all analysis followed, only a "valid" tag of PM2.5 hourly concentration was used.

QA data
Fig. 3: Quality Control of each point (for each hour) are tagged with one of the four types

Figs. 4-5 gives a perspective of PM2.5 daily-concentration with WHO guidelines, US's NAAQS, and Vietnam National Technical Regulation (QCVN 05:2013).

Seasonal patterns are pronounced in sites Hanoi and HCMC with a higher concentration during the winter seasons. The PM2.5 in Hanoi has more days above the QCVN05:2013, which is the most lenient in these three levels. In the total of 1427 days during the period of 1/2016 to 1/2020, HCMC has 66 days above the QCVN05 level and 709 days above the WHO's guideline. The number for Hanoi is 414 and 937 out of the total 1459 hours measured during this period.

For the distribution as shown in Fig. 6, the mean concentration for Hanoi is 46 µg/m3 and HCMC is 27.4 µg/m3. The mean presented for 50% of the hours that have the concentration less than 46 in Hanoi, and 27.4 in HCMC.

Fig. 4: Daily-averaged PM2.5 in Hanoi (1/2016-1/2020)
Fig. 5: Daily-averaged PM2.5 in Hanoi (2016-1/2020)
dist concentration
Fig. 6: Distribution of PM2.5 in Hanoi and HCMC during 2016-1/2020
3.2 Correlation with observed values

The following figures (figs. 7-8) presented the correlation between PM2.5 with wind speed with four directions. The shade of each point was marked for the total precipitation. A heavy rain (0.4-0.5 kg/m2) correlated with a lower PM2.5 which is inline with wet removal. A strong wind (>6m/s) also correlated with a lower PM2.5 in a range of 50µg/m3 or less. The high concentration of PM2.5 in Hanoi observed with light to now wind (<4m/s) and almost no precipitation.

For Hanoi, the East-South wind correlated with a dozen of points with PM2.5 above 250 µg/m3. West-North and North-East contributed a few other high concentration with a light wind. Site HCMC exhibits a long tail of PM2.5 with a low concentration with a strong wind. Heavy rains such as with South-West wind also associated with a lower PM2.5 as in the lower-left graph on Fig. 8.

wind rain
Fig. 7: Correlation of PM2.5 in Hanoi with wind and precipitation
Fig. 8: Correlation of PM2.5 in HCMC with wind and precipitation

Alternatively, we can examine the correlation of PM2.5 with time such as by months and hours (Figs. 9-10). The range of observed concentration is display as the white background inside the black edge. In both sites, the distribution exhibits a long tail toward the high concentration. This suggests multiple sudden measurements of high PM2.5 concentration.

Distribution by month reflects the variations shown in Figs. 4-5 which mostly driven by seasonal weather patterns with an presumption that no significant fluctuation of emission rates of PM2.5 year around.

Interesting differences can be observed in Fig. 10 in which Hanoi's PM2.5 was lower in afternoons and highers during midnight to early mornings. This pattern is inline with frequent temperature inversions in Hanoi observed during the late Fall to early Spring seasons. The temperature inversion occurs at night when the air layer near the ground was cooling faster than the above layer air. The influx of hotter air from South-West with the remain lower temperature air during seasonal transition also created occasion temperature inversion. When this pattern occurs, the vertical mixing is limited and the PM2.5 is kept the near ground at a much higher concentration.

by month
Fig. 9: Distribution of PM2.5 by month
Fig. 10: Distribution of PM2.5 by hour in day
Temperature and relative humidity

The temperature is the primary indicator of the weather conditions. Correlation of the temperature with PM2.5 as shown in Figs. 11 is less obvious. The higher concentration of PM2.5 occurs with a mid-range of the temperature which could be the artifact of more points in the middle. For both sides, lower and higher tails, the PM2.5 concentration is lower. However, when extreme weather occurs, the PM2.5 becomes insignificant.

Fig. 11: Daily-averaged PM2.5 in Hanoi (1/2016-1/2020)

The relative humidity (RH) is an important weather condition that effects the growth of PM2.5 particles. With RH > 70%, that growth becomes significant and a major concern for analyzing the composition and speciation of PM2.5. To reduce the effect of RH on PM2.5 measurement, PM2.5 monitors approved by US EPA (FEM) equipped with a smart heater. The PM2.5 used in this analysis is from FEM monitor such as MetOne BAM-1020 that widely used by US EPA to continuously monitor PM2.5 in ambient air. The underlying assumption here is with proper operation, the effect of RH in ambient air is insignificant to the reliability of PM2.5 reading by such FEM monitors. The standard RH for weight filter in accordance with 40 CRF Part 50, Appendix L is 40%. Other technical references are here (PM2.5 Federal Reference Method, EQPM-0308-170, and Method 201A. A collection of files for PM2.5 continuous monitoring is here.

Fig. 12: Daily-averaged PM2.5 in Hanoi (2016-1/2020)

The RH could be a confusing factor with a high of PM2.5. The confusion stems from the vision reduction possibly associated with a high RH or "mist" condition or with a high PM2.5 concentration. Fig. 13 (left panel) shows the RH with PM2.5 above 100µg/m3. The RH ranges from 50% to over 100% with a more point in close to the condensed level (80-100%). The right panel of Fig. 13 shows with RH >= 90% and the corresponding PM2.5 values. The mean PM2.5 with RH>0.9 is 44µg/m3 which means 50% of sample points with high RH is less than the mean concentration. The mean RH for high PM2.5 is 0.83 or 50% of the sampling point that have 100 or more µg/m3 when RH > 0.83 in the ambient air. The author emphasized that the RH inside the FEM monitor should close to 40% by EPA's requirement. Using both RH and PM2.5 from FEM monitors, we can ascertain if the low vision contributed by high RH in forms of mist or fog or fine particles or both.

extra RH
Fig. 13: Association high PM2.5 (left) and high RH (right) in Hanoi
3.3 Correlation with changes in observed values in Hanoi

The above analysis dealt with the observed values. In the following session, we will analyze the change of the observed values.

With four directions of wind, the largest change either reduction or addition occurs with the wind speed around 2m/s. Strong wind or heavy precipitation seems not lead to significant changes in PM2.5 concentration. This is logical with the air was already low in PM2.5 as show in Figs. 7-8.

hanoi delta
Fig. 14: PM2.5 observed values with the change of wind speed

Figure 15 shows the association of high PM2.5 with the change of wind speed. A high PM2.5 occurs with stagnant air shown in a pine corn shape. Change of wind speed in any direction partially leads to a lower observed PM2.5. Existing strong wind (>6m/s) also leads to a lower PM2.5.

Fig. 15: Change of wind speed with change of PM2.5

Figure 16 provides details of the changes of wind speed and of PM2.5. Stagnant air lead to large fluctuation of observed values which happened mostly during the early mornings and a high observed PM2.5 as shown in Fig. 17.

hanoi delta
Fig. 16: Change of PM2.5 with change of wind speed
Fig. 17: Association large change of PM2.5 with time a day and observed value