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第59期出刊日：2024.10.01

Meteorology-driven PM2.5 interannual variability over East Asia
東亞地區氣象驅動的 PM2.5 年際變化

文/ 大氣科學系 陳正平 教授

Atmospheric fine particulate matter (PM2.5) is a human health risk factor, but its ambient concentration depends on both precursor emissions and meteorology. While emission reductions are used to set PM2.5-related health policies, the effect of meteorology is often overlooked. To explore this aspect, we examined PM2.5 interannual variability (IAV) associated with meteorological parameters using the long-term simulation from the Community Earth System Model (CESM1), a global climate-chemistry model, with fixed emissions. The results are subsequently contrasted with the MERRA-2 reanalysis dataset, which inherently considers emission and meteorology effects.

Over continental East Asia, the CESM1 domain-average PM2.5 IAV is 6.7%, mainly attributed to humidity, precipitation, and ventilation variation. The grid-cell PM2.5 IAVs over southern East China are larger, up to 12% due to the more substantial influence of El Niño-induced meteorological anomalies. Under such climate extreme, sub-regional PM2.5 concentration may occasionally exceed WHO air quality guideline levels despite the compliance of the long-term mean. The simulated PM2.5 IAV over continental East Asia is ~25% of that derived from the MERRA-2 data, which highlights the influence of both emission and meteorology-driven variations and trends inherent in the latter. Although emission-driven variability is significant to PM2.5 IAV, in remote areas downwind of major source regions in East Asia, North America, and Western Europe, the MERRA-2 data revealed that meteorological variations contributed more to PM2.5 IAV than emission variations (points below the identity line in Fig. 1). Thus, when setting policies for complying with the WHO PM2.5-related air quality guideline levels, the highest annual PM2.5 associated with climate extremes (Fig. 2) should be considered instead of that based on average climate conditions.
[contents from: Wang, C.-Y., J.-P. Chen, and W.-C. Wang, 2023: Meteorology-driven PM2.5 interannual variability over East Asia. Sci. Total Environ., 904, 166911.]

 
Fig. 1: The relationship between PM2.5 and emission IAV in the sub-regions of EA (red), NA (blue), and WE (green). The numbers in the right graph were calculated from the 1980–2008 MERRA2 data, and the numbers on the left are the CESM1 simulation results with fixed emission (emission IAV=0). The regional maps with corresponding block numbers are given in insets at the top. The solid grey line is the identity line.
 
Fig. 2: Sub-regional surface PM2.5 concentration over East Asia. The data are permutated in decreasing order of the PM2.5 range, with corresponding block numbers shown in the inset on the upper right. The horizontal dashed lines indicate the WHO air quality guideline values (GV) and different interim targets (IT). The PM2.5 concentration of guideline values GV05 and GV21 are 10 and 5 μg m3 set in 2005 and 2021, respectively. IT-2, IT-3, and IT-4 are 25, 15, and 10 μg m3.
 
大氣細懸浮微粒（PM2.5）是人類健康的危險因子，其濃度取決於前驅物排放量和氣象影響。PM2.5相關的健康政策的制訂一般是透過排放量的控制，而氣象的影響常常被忽略。為了探索後者的重要性，我們使用社區地球系統模式CESM1以固定年排放量的方式進行純粹氣象驅動的PM2.5年際變化（IAV）的長期模擬。隨後將結果與 MERRA-2 再分析資料集進行對比，後者本質上同時考慮了排放和氣象影響。
在東亞大陸，CESM1的PM2.5區域平均IAV為6.7%，主要歸因於濕度、降水和通風變化。華東南部地區網格單元PM2.5 IAV高達12%，主要受厄爾尼諾引發的氣象異常影響。某些區域儘管PM2.5濃度長期平均值符合世界衛生組織空氣品質指南的濃度水準，但在極端氣候狀況下卻可能會超標。在東亞地區，CESM模擬的PM2.5 IAV約為MERRA-2數據的25%，這突顯了排放和氣象驅動的共同作用，以及後者長期趨勢的重要性。儘管MERRA-2數據顯示，排放的變異對PM2.5 IAV影響很大，但處於東亞、北美和西歐主要排放源區下風處的偏遠地區，氣象變化對 PM2.5 IAV的貢獻卻是大於排放變化(圖1對角線下方)。因此，在製定符合世界衛生組織 PM2.5目標濃度的政策時，應考慮與極端氣候相關的最高年度PM2.5（圖2），而不是基於平均氣候條件的濃度值。