夏收時(shí)段農(nóng)村大氣亞微米顆粒物數(shù)濃度分布特征

鄭淑睿,孔少飛*,嚴(yán) 沁,吳 劍,朱戈昊,楊國(guó)威,吳方琪,牛真真,鄭明明,,鄭 煌,程 溢,陳 楠 ,許 可,祁士華,3

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夏收時(shí)段農(nóng)村大氣亞微米顆粒物數(shù)濃度分布特征

鄭淑睿1,孔少飛1*,嚴(yán) 沁1,吳 劍1,朱戈昊1,楊國(guó)威1,吳方琪1,牛真真1,鄭明明1,2,鄭 煌1,程 溢1,陳 楠2,許 可2,祁士華1,3

(1.中國(guó)地質(zhì)大學(xué)(武漢)環(huán)境學(xué)院,湖北 武漢 430074；2.湖北省環(huán)境監(jiān)測(cè)中心站,湖北 武漢 430074；3.中國(guó)地質(zhì)大學(xué)(武漢),生物地質(zhì)與環(huán)境地質(zhì)國(guó)家重點(diǎn)實(shí)驗(yàn)室,湖北 武漢 430074)

為了從源區(qū)的角度研究華北平原夏收時(shí)段大氣亞微米顆粒物粒徑譜分布,采用掃描電遷移率粒徑譜儀,于2017年6月對(duì)華北平原典型農(nóng)村點(diǎn)位亞微米顆粒物數(shù)濃度進(jìn)行連續(xù)觀測(cè).結(jié)果表明,觀測(cè)期間大氣亞微米顆粒物粒徑分布主要集中在小于300nm處,平均數(shù)濃度為28371cm-3.不同模態(tài)顆粒物數(shù)濃度分布差異明顯,核模態(tài)(< 20nm)呈線性分布,愛(ài)根核模態(tài)(20~100nm)呈多項(xiàng)分布,積聚模態(tài)(>100nm)呈對(duì)數(shù)分布.48h后向軌跡聚類結(jié)果表明,觀測(cè)點(diǎn)位氣團(tuán)受其東部的江蘇省、山東省和安徽省生物質(zhì)燃燒傳輸影響時(shí),顆粒物總數(shù)濃度增加66.7%.潛在源貢獻(xiàn)因子法和濃度權(quán)重軌跡法,表明潛在源區(qū)為觀測(cè)點(diǎn)位以東的區(qū)域,且以粒徑小于100nm的顆粒物為主.

華北平原；夏收時(shí)段；農(nóng)村點(diǎn)位；氣溶膠數(shù)濃度；生物質(zhì)燃燒；影響源區(qū)

亞微米顆粒物為空氣動(dòng)力學(xué)直徑(p)小于1μm的顆粒物,是大氣細(xì)顆粒物主要成分.根據(jù)粒徑大小,可分為核模態(tài)(p￡20nm)、愛(ài)根核模態(tài)(20100nm)[1-2].因亞微米粒子吸收光和散射光的特性,中國(guó)頻發(fā)的霧霾事件與此密切相關(guān)[3].亞微米顆粒面積大,易富集有毒有害物質(zhì),可直接進(jìn)入血液,極易誘發(fā)呼吸系統(tǒng)和心血管等疾病[4],對(duì)人體健康造成危害.因而,當(dāng)前基于各種在線監(jiān)測(cè)儀器,對(duì)亞微米顆粒物的粒徑分布[5]和化學(xué)組成[6]的研究已成為熱點(diǎn).

生物質(zhì)燃燒對(duì)區(qū)域空氣質(zhì)量的影響一直是學(xué)術(shù)研究焦點(diǎn),其排放的污染物經(jīng)過(guò)長(zhǎng)距離傳輸會(huì)對(duì)區(qū)域內(nèi)城市和背景點(diǎn)位顆粒物數(shù)濃度產(chǎn)生影響.Ma等[7]、Zhang等[8]和張丹等[9]通過(guò)室內(nèi)模擬燃燒實(shí)驗(yàn)研究發(fā)現(xiàn),生物質(zhì)燃燒排放一次顆粒物數(shù)濃度高達(dá)104~105cm-3.王紅磊[10]、Betha等[11]和Shang等[12]通過(guò)外場(chǎng)觀測(cè)實(shí)驗(yàn)研究發(fā)現(xiàn),受生物質(zhì)燃燒影響時(shí),大氣顆粒物數(shù)濃度增加1.2~3.3倍,并伴隨大量新粒子生成.Bi等[13]檢測(cè)到大氣中生物質(zhì)燃燒排放顆粒物在亞微米顆粒物中,占比高達(dá)20.3%.

不僅僅是數(shù)濃度的影響,生物質(zhì)燃燒排放的污染物也會(huì)影響到區(qū)域大氣環(huán)境中氣溶膠的粒徑分布特征.多位學(xué)者研究表明[8,14-16],受到生物質(zhì)燃燒影響時(shí),顆粒物主要集中在積聚模態(tài),中位徑為50~200nm.張丹等[9]研究發(fā)現(xiàn),生物質(zhì)燃燒排放一次顆粒物數(shù)濃度呈對(duì)數(shù)雙峰分布.Ma等[7]研究發(fā)現(xiàn),不同生物質(zhì)燃燒排放顆粒物既有雙峰分布也有單峰分布,且隨時(shí)間向大粒徑方向偏移.

生物質(zhì)開放燃燒極易誘發(fā)重污染事件.當(dāng)前,生物質(zhì)燃燒對(duì)于空氣質(zhì)量的影響多集中在顆粒物的質(zhì)量濃度[17-19]、吸濕性[20-21]及光化學(xué)特性[22-23],對(duì)于顆粒物的數(shù)濃度研究相對(duì)較少.少量對(duì)于顆粒物數(shù)濃度的研究多集中在大城市或背景區(qū)域,缺少在農(nóng)村也即生物質(zhì)燃燒排放源區(qū),同時(shí)也是傳輸過(guò)程關(guān)鍵區(qū)域的研究.因此,在農(nóng)村相對(duì)其他源類較少的環(huán)境條件下,對(duì)于大氣氣溶膠粒徑分布特征以及從初始燃燒排放到傳輸轉(zhuǎn)化過(guò)程中氣溶膠數(shù)濃度的演化缺乏認(rèn)知.基于此,本研究選擇華北典型農(nóng)村點(diǎn)位,在夏收時(shí)段,對(duì)其氣溶膠數(shù)濃度進(jìn)行連續(xù)觀測(cè),以期為秸稈焚燒密集階段區(qū)域大氣污染防控以及秸稈焚燒排放氣溶膠傳輸老化的認(rèn)知提供參考和借鑒.

1 材料與方法

1.1 樣品采集

如圖1,采樣地點(diǎn)位于河南省平頂山市寶豐縣肖旗鄉(xiāng)(33.88°N 113.04°E),采樣高度距離地面12m.采樣點(diǎn)距離縣城約5km,其東南方有小學(xué),東臨S241省道,其他方向?yàn)榇竺娣e農(nóng)田.該處屬于暖溫帶,為半濕潤(rùn)大陸性季風(fēng)氣候.該區(qū)域整體地勢(shì)為西高東低,西部最高山峰為無(wú)名山峰(海拔740m),東、西部多山,中部平原和丘陵相間.觀測(cè)時(shí)間為2017年6月8日~ 2017年6月15日,共8d.

1.2 實(shí)驗(yàn)儀器與資料來(lái)源

采樣儀器為德國(guó)Grimm公司生產(chǎn)的SMPS+E掃描電遷移率粒徑譜儀(SMPS),儀器包括對(duì)顆粒物進(jìn)行分類的差分粒子電遷移器和對(duì)顆粒物進(jìn)行計(jì)數(shù)的法拉第靜電沉降器.采樣過(guò)程中,掃描粒徑范圍設(shè)定為10.27~1093.80nm,鞘氣比為3,共225個(gè)通道,時(shí)間分辨率為20min.

火點(diǎn)數(shù)據(jù)和后向軌跡數(shù)據(jù)來(lái)源于美國(guó)國(guó)家海洋和大氣管理局(NOAA).氣壓、氣溫、能見(jiàn)度、風(fēng)向、風(fēng)速等氣象要素?cái)?shù)據(jù)來(lái)源于當(dāng)?shù)貧庀缶?時(shí)間分辨率為3h.

圖1 采樣點(diǎn)位氣團(tuán)后向軌跡聚類及同期火點(diǎn)分布

1.3 分析方法

1.3.1 軌跡聚類分析 軌跡聚類分析采用NOAA研發(fā)的HYSPLIT4模式,對(duì)來(lái)自美國(guó)國(guó)家環(huán)境預(yù)報(bào)中心(NCEP)的全球資料同化系統(tǒng)(GDAS)的氣象資料進(jìn)行計(jì)算.本次計(jì)算的起點(diǎn)高度對(duì)應(yīng)為離地高度500m,氣團(tuán)軌跡后推時(shí)間為48h,時(shí)間分辨率為1h,共得到192條軌跡線,以此反映觀測(cè)點(diǎn)位的氣團(tuán)移動(dòng)特征.

1.3.2 潛在源貢獻(xiàn)因子分析 潛在源貢獻(xiàn)因子分析[24-25](PSCF)是利用后向軌跡來(lái)描述可能污染源區(qū)方位的條件概率函數(shù).PSCF定義為式(1):

式中:、為網(wǎng)格的經(jīng)緯度;m為經(jīng)過(guò)(,)網(wǎng)格的污染軌跡端點(diǎn)數(shù);n為落在(,)網(wǎng)格的所有軌跡端點(diǎn)數(shù).因網(wǎng)格點(diǎn)與觀測(cè)點(diǎn)位間距離的差異及網(wǎng)格內(nèi)氣團(tuán)停留時(shí)間的差異,PSCF值存在波動(dòng),因而,引入權(quán)重函數(shù)(n)來(lái)進(jìn)行降誤差處理,定義為式(2):

PSCF值越高表明該網(wǎng)格對(duì)觀測(cè)點(diǎn)位的濃度貢獻(xiàn)越大,即PSCF值高的網(wǎng)格被解釋為潛在源區(qū).

1.3.3 濃度權(quán)重軌跡分析 PSCF反映的是網(wǎng)格(,)中污染軌跡所占比值,不能區(qū)分該網(wǎng)格點(diǎn)對(duì)觀測(cè)點(diǎn)位污染程度的大小.因而,引入濃度權(quán)重軌跡分析[26](CWT)以反映不同軌跡的污染程度,即CWT可模擬潛在源區(qū)污染物權(quán)重濃度值.CWT定義為公式(3):

式中:、為網(wǎng)格的經(jīng)緯度;C為網(wǎng)格(,)的平均權(quán)重濃度;為軌跡;C為軌跡經(jīng)過(guò)網(wǎng)格(,)的顆粒物數(shù)濃度;為軌跡經(jīng)過(guò)網(wǎng)格(,)的滯留時(shí)間;(n)為權(quán)重函數(shù),與PSCF分析法相同.

2 結(jié)果與討論

2.1 觀測(cè)期間空氣質(zhì)量與天氣形勢(shì)分析

圖2 觀測(cè)點(diǎn)位氣象要素時(shí)間變化

2017年6月8日~6月10日,觀測(cè)點(diǎn)位氣象要素隨時(shí)間的變化(圖2)較為穩(wěn)定;6月11日,氣壓有升高的趨勢(shì);13日~15日,氣壓維持較高值.觀測(cè)期間能見(jiàn)度均值為8.1km,大部分時(shí)段為霾,最低值出現(xiàn)在9日5:00為2.0km.氣溫的逐日變化趨勢(shì)較為一致,最小值出現(xiàn)在5:00附近,最大值出現(xiàn)在12:00附近;相對(duì)濕度變化趨勢(shì)與溫度相反.觀測(cè)期間風(fēng)速變化范圍為0~6m/s,風(fēng)向較為多變.其中,11日~15日觀測(cè)期間,500hPa高空天氣圖上東亞地區(qū)中高緯為“一槽一脊”型,東亞沿海槽呈東北-西南走向,貝加爾湖附近的脊區(qū)為阻塞形勢(shì),觀測(cè)地點(diǎn)處于中高緯槽脊底部多波動(dòng)的偏西風(fēng)場(chǎng)中,天氣尺度形勢(shì)較穩(wěn)定,無(wú)明顯冷空氣南下入侵.850hPa等壓面圖上,西太平洋副熱帶高壓北側(cè)的梅雨鋒穩(wěn)定在長(zhǎng)江沿線,該點(diǎn)位處于華北反氣旋底部,受較弱的偏東風(fēng)場(chǎng)控制,對(duì)大氣污染物的垂直擴(kuò)散有抑制作用.地面天氣圖上,觀測(cè)點(diǎn)位于較弱的染物在西部山前堆積,且東北風(fēng)風(fēng)速僅1~3m/s,大氣水平擴(kuò)散能力差,利于污染物長(zhǎng)時(shí)間堆積.

利用TrajStat對(duì)觀測(cè)期間的192條后向軌跡進(jìn)行計(jì)算,聚類分析得到6條主要傳輸路徑(圖1).聚類1~2及4~6均為大陸氣團(tuán),其中,聚類1、2和4主要來(lái)自采樣點(diǎn)位東部的江蘇、山東和安徽省,聚類5來(lái)自南部的湖北省,聚類6來(lái)自西北方的內(nèi)蒙古自治區(qū).聚類3源自東海,途徑江蘇和安徽省,為偏東海洋氣團(tuán).王愛(ài)平等[26]指出,軌跡路線和方向表示氣團(tuán)達(dá)到觀測(cè)點(diǎn)位途徑的地區(qū),軌跡長(zhǎng)短表示氣團(tuán)的移動(dòng)速度,軌跡長(zhǎng)對(duì)應(yīng)移動(dòng)快速的氣團(tuán),軌跡短則對(duì)應(yīng)移動(dòng)緩慢的氣團(tuán).因而,聚類3和6的氣團(tuán)移動(dòng)速度快.觀測(cè)期間我國(guó)華北平原和長(zhǎng)江三角洲地區(qū)火點(diǎn)密集,河北、山東及江蘇省火點(diǎn)數(shù)多.但氣團(tuán)并非來(lái)自火點(diǎn)數(shù)最密集的河北省.受地勢(shì)西高東低影響,產(chǎn)生東風(fēng)時(shí),污染物易堆積不易擴(kuò)散,使得氣流主要來(lái)自觀測(cè)點(diǎn)位以東的區(qū)域.聚類1~4明顯受到東部夏收時(shí)段生物質(zhì)燃燒傳輸影響,占總軌跡數(shù)的69.3%;聚類6受到西北較弱的燃燒排放傳輸影響,發(fā)生頻率為11.0%;聚類5則主要受其他源排放影響.

2.2 觀測(cè)期間顆粒物數(shù)濃度粒徑分布特征

2.2.1 數(shù)濃度譜粒徑分布 圖3為觀測(cè)期間大氣氣溶膠數(shù)濃度平均分布,本次研究將10.27￡p￡20nm定義為核模態(tài),20

其中,核模態(tài)顆粒物數(shù)濃度占總數(shù)濃度37.0%,愛(ài)根核模態(tài)占42.9%,積聚模態(tài)僅占20.1%.顆粒物數(shù)濃度主要集中在核模態(tài)及愛(ài)根核模態(tài).這可能是由于夏季氣溫高,利于新粒子生成,使得小粒徑顆粒物數(shù)濃度較高[27].顆粒物平均質(zhì)量濃度主要集中在積聚模態(tài),核模態(tài)與愛(ài)根核模態(tài)顆粒物數(shù)濃度高,但因其體積小,質(zhì)量濃度偏低.因而,該農(nóng)村點(diǎn)位夏季大氣顆粒物數(shù)濃度以核模態(tài)和愛(ài)根核模態(tài)的超細(xì)顆粒物為主(79.9%),質(zhì)量濃度以積聚模態(tài)的大粒徑顆粒物為主(98.3%).

圖3 大氣氣溶膠數(shù)濃度平均譜和三模態(tài)譜分布

圖4 不同氣團(tuán)聚類背景下三模態(tài)顆粒物數(shù)濃度分布

圖4中,三模態(tài)顆粒物數(shù)濃度分布差異明顯:核模態(tài)呈線性分布,數(shù)濃度隨粒徑增加,基本為上升趨勢(shì),僅對(duì)于聚類4的氣團(tuán)為下降趨勢(shì);愛(ài)根核模態(tài)呈多項(xiàng)分布,60~80nm存在明顯峰值;積聚模態(tài)數(shù)濃度呈對(duì)數(shù)分布,主要集中在100~300nm處.核模態(tài)中,受生物質(zhì)燃燒傳輸影響的氣團(tuán)軌跡影響下,顆粒物數(shù)濃度均高于聚類5氣團(tuán)的數(shù)濃度;聚類4氣團(tuán)的顆粒物數(shù)濃度最高.愛(ài)根核模態(tài)中,不同聚類氣團(tuán)的顆粒物數(shù)濃度粒徑分布沒(méi)有明顯的變化規(guī)律,聚類4數(shù)濃度仍呈隨粒徑增大而下降的趨勢(shì).積聚模態(tài)中,聚類1~4氣團(tuán)的顆粒物濃度較氣團(tuán)5~6的數(shù)濃度增加85.9%,且聚類1~4氣團(tuán)對(duì)應(yīng)的數(shù)濃度與粒徑分布差別不明顯.

表1對(duì)不同背景聚類的氣象要素與氣溶膠數(shù)濃度特征進(jìn)行統(tǒng)計(jì),氣團(tuán)1~4對(duì)應(yīng)的天氣條件為:氣壓高、氣溫高、且相對(duì)濕度低,利于顆粒物形成并堆積,對(duì)應(yīng)的核模態(tài)和愛(ài)根核模態(tài)數(shù)濃度高;而氣團(tuán)5和6對(duì)應(yīng)的顆粒物數(shù)濃度低,相對(duì)較清潔.氣團(tuán)5~6數(shù)濃度均值僅為7.2×103cm-3,氣團(tuán)1~4數(shù)濃度均值增加66.7%.不同氣團(tuán)聚類背景下,核模態(tài)與愛(ài)根核模態(tài)粒徑分布相似,積聚模態(tài)則出現(xiàn)明顯的數(shù)濃度差異.受生物質(zhì)燃燒排放傳輸影響,顆粒物大粒徑數(shù)濃度明顯增加.

表1 觀測(cè)期間每組氣團(tuán)聚類的氣象要素特征及氣溶膠數(shù)濃度特征

2.2.2 顆粒物數(shù)濃度粒徑譜日變化分析 觀測(cè)期間氣溶膠數(shù)濃度日變化分布波動(dòng)較大(圖5),積聚模態(tài)顆粒物數(shù)濃度峰值出現(xiàn)在08:00和24:00;愛(ài)根核模態(tài)數(shù)濃度峰值出現(xiàn)在01:00、08:00、13:00、18:00和22:00;核模態(tài)峰值出現(xiàn)在02:00、07:00、11:00、15:00和22:00.積聚模態(tài)顆粒物數(shù)濃度維持較低濃度,平均壽命長(zhǎng),日變化范圍小[28].下午邊界層高度抬升,近地面數(shù)濃度減小,積聚模態(tài)顆粒物數(shù)濃度出現(xiàn)低值.因新粒子形成并增長(zhǎng)成愛(ài)根核模態(tài),使得愛(ài)根核模態(tài)顆粒物數(shù)濃度峰值稍滯后于核模態(tài)[29].太陽(yáng)輻射有利于顆粒物成核,觀測(cè)時(shí)段為夏季,因而從05:00開始核模態(tài)濃度迅速增高;隨后因凝結(jié)碰并增長(zhǎng),從07:00開始核模態(tài)顆粒物數(shù)濃度下降;11:00再次出現(xiàn)高值,可能與08:00~09:00時(shí)段農(nóng)村點(diǎn)位污染源排放大量氣態(tài)污染物的成核增長(zhǎng)有關(guān).05:00~ 08:00,愛(ài)根核膜態(tài)和積聚模態(tài)氣溶膠數(shù)濃度也因此快速增加.

中午太陽(yáng)輻射強(qiáng),顆粒物成核增強(qiáng);太陽(yáng)輻射強(qiáng),氣溫高,大氣光化學(xué)反應(yīng)增強(qiáng),二次氣溶膠增加,愛(ài)根核膜態(tài)顆粒物數(shù)濃度維持高值[30];同時(shí)因氣溫高,相對(duì)濕度較低,氣溶膠粒子易滯留在空中;因而,中午的顆粒物數(shù)濃度為全天最高.午后,光化學(xué)反應(yīng)減弱,及顆粒物凝結(jié)碰并成核作用,顆粒物數(shù)濃度降低[31].夜晚19:00~22:00,各模態(tài)顆粒物數(shù)濃度均增高,除與人為活動(dòng)如烹調(diào)油煙等排放一次氣態(tài)污染物成核和一次顆粒態(tài)污染物直接貢獻(xiàn)外;夜晚氣溫低,大氣層結(jié)穩(wěn)定,不利于顆粒物擴(kuò)散[32],也會(huì)導(dǎo)致三模態(tài)顆粒物數(shù)濃度在夜間都相對(duì)較高.

圖5 不同模態(tài)大氣氣溶膠數(shù)濃度日變化分布

觀測(cè)期間,因受秸稈燃燒排放傳輸過(guò)程影響,整體顆粒物數(shù)濃度偏高,日變化波動(dòng)大,存在多個(gè)峰值.與城區(qū)顆粒物數(shù)濃度日變化相比存在明顯差異:城區(qū)核模態(tài)顆粒物數(shù)濃度峰值出現(xiàn)在10:00;愛(ài)根核模態(tài)顆粒物數(shù)濃度峰值分別出現(xiàn)在08:00、13:00及20:00的早、中、晚上下班高峰時(shí)段;積聚模態(tài)顆粒物數(shù)濃度峰值發(fā)生時(shí)段也與早晚高峰時(shí)段對(duì)應(yīng)[33].城區(qū)氣溶膠三模態(tài)變化趨勢(shì)與交通源、太陽(yáng)輻射及混合層高度緊密相關(guān).而本研究受交通源排放的影響小,受生物質(zhì)燃燒傳輸?shù)挠绊戄^大,因而表現(xiàn)出不同的氣溶膠數(shù)濃度日變化趨勢(shì).

2.2.3 與其他地方比較 對(duì)比城區(qū)、郊區(qū)、山區(qū)及農(nóng)村不同區(qū)域氣溶膠數(shù)濃度的觀測(cè)結(jié)果(表2),城區(qū)與郊區(qū)的顆粒物數(shù)濃度較高,其次為農(nóng)村點(diǎn)位顆粒物數(shù)濃度,山區(qū)顆粒物數(shù)濃度最低.超細(xì)顆粒物(粒徑小于100nm的氣溶膠粒子)數(shù)濃度在亞微米顆粒物中的占比為46.9%~95.5%,平均為75.7%.受區(qū)域類型、季節(jié)、儀器測(cè)量范圍以及不同主導(dǎo)源排放的影響,大氣顆粒物粒徑分布存在明顯差異.

城區(qū)顆粒物數(shù)濃度主要集中在核模態(tài)及愛(ài)根核模態(tài),總數(shù)濃度達(dá)104cm-3.北京城區(qū)年平均與夏季平均分布一致,受其他季節(jié)不同源排放影響,總數(shù)濃度存在季節(jié)差異[29,32].與其他城區(qū)相比,福建城區(qū)[28]顆粒物粒徑測(cè)量范圍達(dá)2μm,可能為積聚模態(tài)顆粒物數(shù)濃度占比偏高的原因.

發(fā)達(dá)城市附近郊區(qū)顆粒物數(shù)濃度分布與城區(qū)類似.陜西西安郊區(qū)[34]的顆粒物總數(shù)濃度偏低,且粒徑分布向愛(ài)根核模態(tài)與積聚模態(tài)的大粒徑方向偏移.2015年北京郊區(qū)[35]夏季與冬季亞微米顆粒物總數(shù)濃度為9.6×103cm-3和1.39×104cm-3,受冬季供暖燃煤、生物質(zhì)燃燒及其它人類活動(dòng)排放源影響,總數(shù)濃度增加44.7%.其中,愛(ài)根核模態(tài)數(shù)濃度上升超過(guò)50%,核模態(tài)和積聚模態(tài)分別上升43.5%與38.2%.

除山東泰山山區(qū)[36]外,其他山區(qū)的顆粒物數(shù)濃度較低[12,27,37-38],為103cm-3,超細(xì)顆粒物數(shù)濃度占比為46.91%~79.13%.在2008年春季[37]與2014年夏季[27]安徽黃山山區(qū)的兩次觀測(cè)表明,小粒徑顆粒物數(shù)濃度變化差異小,因季節(jié)和年份差異,夏季大粒徑范圍顆粒物數(shù)濃度較春季明顯增加.

表2 不同地區(qū)不同時(shí)段氣溶膠數(shù)濃度觀測(cè)結(jié)果對(duì)比

農(nóng)村顆粒物數(shù)濃度分布低于城區(qū)與郊區(qū),但高于山區(qū).本研究中,超細(xì)顆粒物總數(shù)濃度為28371cm-3,僅低于河北城區(qū)[31]、北京城區(qū)[29]與上海郊區(qū)[39].本研究處于夏收時(shí)段,受周圍生物質(zhì)燃燒排放傳輸影響,氣溶膠數(shù)濃度處于較高水平,遠(yuǎn)高于文獻(xiàn)中報(bào)道的3個(gè)農(nóng)村點(diǎn)位[40-42].

2.3 潛在影響源區(qū)分布

利用PSCF和CWT對(duì)三模態(tài)顆粒物數(shù)濃度進(jìn)行潛在來(lái)源分析.圖6中,潛在貢獻(xiàn)源因子分布相似.觀測(cè)點(diǎn)位的西北方無(wú)明顯的PSCF高值分布,可能是由于該區(qū)域距離遠(yuǎn)且顆粒物數(shù)濃度較低,傳輸至觀測(cè)點(diǎn)位時(shí),已影響較小的原因;且因計(jì)算網(wǎng)格與觀測(cè)點(diǎn)位的距離增加可能導(dǎo)致其不確定性的增加.高PSCF值(PSCF>0.6)區(qū)域主要集中在觀測(cè)點(diǎn)位東部的江蘇中北部、安徽北部、山東西南部及河南省中東部區(qū)域,該區(qū)域?yàn)榛瘘c(diǎn)密集區(qū),東部區(qū)域生物質(zhì)燃燒排放傳輸對(duì)觀測(cè)點(diǎn)位顆粒物數(shù)濃度貢獻(xiàn)較大.

CWT分析結(jié)果顯示(圖7),強(qiáng)潛在源區(qū)為觀測(cè)點(diǎn)位以東的區(qū)域,西部區(qū)域數(shù)濃度貢獻(xiàn)較弱.觀測(cè)點(diǎn)位以西的區(qū)域,CWT三模態(tài)數(shù)值分布相似,均為低值區(qū);對(duì)于東部區(qū)域,位于小粒徑的核模態(tài)與愛(ài)根核模態(tài)對(duì)應(yīng)的CWT值較高,積聚核模態(tài)對(duì)應(yīng)的CWT值低.由此可見(jiàn),生物質(zhì)燃燒污染物排放傳輸,主要集中在核模態(tài)與愛(ài)根核模態(tài)的小粒徑范圍.

圖6 觀測(cè)期間三模態(tài)顆粒物數(shù)濃度潛在貢獻(xiàn)源因子分布(PSCF)

圖7 觀測(cè)期間三模態(tài)顆粒物數(shù)濃度權(quán)重分析(CWT)

3 結(jié)論

3.1 夏收時(shí)段華北平原某農(nóng)村點(diǎn)位大氣亞微米顆粒物粒徑分布主要集中在小于300nm處,呈三峰型分布,峰值分別位于28.67nm、62.32nm及141.70nm,平均總數(shù)濃度為28371cm-3,遠(yuǎn)高于文獻(xiàn)中報(bào)道的其他農(nóng)村點(diǎn)位顆粒物數(shù)濃度.

3.2 顆粒物數(shù)濃度日變化主要與太陽(yáng)輻射、邊界層高度、氣溫、相對(duì)濕度和人為活動(dòng)有關(guān).積聚模態(tài)顆粒物日變化穩(wěn)定,愛(ài)根核與核模態(tài)粒子日變化波動(dòng)大,存在多個(gè)峰值.午后因太陽(yáng)輻射強(qiáng),氣溫高,光化學(xué)反應(yīng)強(qiáng),相對(duì)濕度低,顆粒物數(shù)濃度達(dá)最高值.

3.3 觀測(cè)期間,三模態(tài)顆粒物數(shù)濃度分布差異明顯:核模態(tài)呈線性分布;愛(ài)根核模態(tài)呈多項(xiàng)分布;積聚模態(tài)呈對(duì)數(shù)分布.受生物質(zhì)燃燒污染物排放傳輸過(guò)程影響的氣團(tuán)對(duì)應(yīng)的顆粒物數(shù)濃度偏高,其中,積聚模態(tài)顆粒物數(shù)濃度增加85.9%.

3.4 利用PSCF和CWT對(duì)三模態(tài)顆粒物數(shù)濃度進(jìn)行潛在來(lái)源分析,高濃度源區(qū)主要集中在觀測(cè)點(diǎn)位東部的江蘇中北部、安徽北部、山東西南部及河南中東部區(qū)域.三模態(tài)的PSCF分布相似,核模態(tài)和愛(ài)根核模態(tài)對(duì)應(yīng)的CWT較高,積聚模態(tài)顆粒物對(duì)應(yīng)的CWT值則較低.

[1] Hussein T. Indoor and outdoor aerosol particle size characterization in Helsinki [D]. Report Series in Aerosol Science, 2005,74:1-53.

[2] Hussein T, Daso M D, Pet?j? T, et al. Evaluation of an automatic algorithm for fitting the particle number size distributions [J]. Boreal environment research, 2005,10(5):337-355.

[3] Che H Z, Xia X G, Zhu J, et al. Aerosol optical properties under the condition of heavy haze over an urban site of Beijing, China [J]. Environmental Science and Pollution Research, 2015,22(2):1043- 1053.

[4] 趙金鐲,宋偉民.大氣超細(xì)顆粒物的分布特征及其對(duì)健康的影響[J]. 環(huán)境與職業(yè)醫(yī)學(xué), 2007,24(1):76-79. Zhao J Z, Song W M. The distributing character and composition of ultrafine particles and its effects on human health [J]. Journal of Environmental and Occupational Medicine, 2007,24(1):76-79.

[5] Cheung H C, Chou C C K, Chen M J, et al. Seasonal variations of ultra-fine and submicron aerosols in Taipei, Taiwan: implications for particle formation processes in a subtropical urban area [J]. Atmospheric Chemistry and Physics, 2016,16(3):1317-1330.

[6] Du W, Sun Y L, Xu Y S, et al. Chemical characterization of submicron aerosol and particle growth events at a national background site (3295m a.s.l.) on the Tibetan Plateau [J]. Atmospheric Chemistry and Physics, 2015,15(9):13515-13550.

[7] Ma Y, Chen C, Wang J F, et al. Evolution in physiochemical and cloud condensation nuclei activation properties of crop residue burning particles during photochemical aging [J]. Journal of Environmental Sciences, 2019,77:43-53.

[8] Zhang H F, Hu D W, Chen J M, et al. Particle Size Distribution and Polycyclic Aromatic Hydrocarbons Emissions from Agricultural Crop Residue Burning [J]. Environmental Science and Technology, 2011, 45(13):5477-5482.

[9] 張 丹,趙 麗,陳剛才,等.不同燃燒過(guò)程顆粒物粒徑排放特征[J]. 中國(guó)環(huán)境科學(xué), 2015,35(11):3239-3246. Zhang D, Zhao L, Chen G C, et al. The particle size distribution characteristics of different combustion sources [J]. China Environmental Science, 2015,35(11):3239-3246.

[10] 王紅磊.南京市大氣氣溶膠理化特征觀測(cè)研究[D]. 南京:南京信息工程大學(xué), 2013. Wang H L. Observation study of the aerosol’s characteristics in the city of Nanjing [D]. Nanjing University of Information Science and Technology, 2013.

[11] Betha R, Zhang Z, Balasubramanian R. Influence of trans-boundary biomass burning impacted air masses on submicron particle number concentrations and size distributions [J]. Atmospheric Environment, 2014,92:9-18.

[12] Shang D J, Hu M, Zheng J, et al. Particle size distribution and new particle formation under influence of biomass burning at a high altitude background site of Mt. Yulong (3410m) in China [J]. Atmospheric Chemistry and Physics, 2018,18(21):15687-15703.

[13] Bi X H, Zhang G H, Li L, et al. Mixing state of biomass burning particles by single particle aerosol mass spectrometer in the urban area of PRD, China [J]. Atmospheric Environment, 2011,45(20):3447- 3453.

[14] Rissler J, Swietlicki E, Zhou J, et al. Physical properties of the sub-micrometer aerosol over the Amazon rain forest during the wet-to- dry season transition – comparison of modeled and measured CCN concentrations [J]. Atmospheric Chemistry and Physics, 2004,4:2119-2143.

[15] Chen J M, Li C L, Ristovski Z, et al. A review of biomass burning: Emissions and impacts on air quality, health and climate in China [J]. Science of the Total Environment, 2017,579:1000-1034.

[17] Paglione M, Saarikoski S, Carbone S, et al. Primary and secondary biomass burning aerosols determined by proton nuclear magnetic resonance (1H-NMR) spectroscopy during the 2008EUCAARI campaign in the Po Valley (Italy) [J]. Atmospheric Chemistry and Physics, 2014,14:5089–5110.

[18] Li W J, Shao L Y, Buseck P R. Haze types in Beijing and the influence of agricultural biomass burning [J]. Atmospheric Chemistry and Physics, 2010,10(17):8119–8130.

[19] Engling G, He J, Betha R, et al. Assessing the regional impact of indonesian biomass burning emissions based on organic molecular tracers and chemical mass balance modeling [J]. Atmospheric Chemistry and Physics, 2014,14(14):8043–8054.

[20] Bougiatioti A, Bezantakos S, Stavroulas I, et al. Biomass-burning impact on CCN number, hygroscopicity and cloud formation during summertime in the eastern Mediterranean [J]. Atmospheric Chemistry and Physics, 2016,16(11):7389–7409.

[21] Li C L, Ma Z, Chen J M, et al. Evolution of biomass burning smoke particles in the dark [J]. Atmospheric Environment, 2015,120:244- 252.

[22] Zhai J H, Lu X H, Li L, et al. Size-resolved chemical composition, effective density, and optical properties of biomass burning particles [J]. Atmospheric Chemistry and Physics, 2017,17(12):7481–7493.

[23] Zhang S L, Ma N, Kecorius S, et al. Mixing state of atmospheric particles over the North China Plain [J]. Atmospheric Environment, 2016,125:125-164.

[24] 王郭臣,王 玨,信玉潔,等.天津PM10和NO2輸送路徑及潛在源區(qū)研究[J]. 中國(guó)環(huán)境科學(xué), 2014,34(12):3009-3016. Wang G C, Wang J, Xin Y J, et al. Transportation pathways and potential source areas of PM10and NO2in Tianjin [J]. China Environmental Science, 2014,34(12):3009-3016.

[25] 張 磊,金蓮姬,朱 彬,等.2011年6-8月平流輸送對(duì)黃山頂污染物濃度的影響[J]. 中國(guó)環(huán)境科學(xué), 2013,33(6):969-978. Zhang L, Jin L J, Zhu B, et al. The influence of advective transport on the concentrations of pollutants at the top of mountain Huangshan from June to August, 2011 [J]. China Environmental Science, 2013, 33(6):969-978.

[26] 王愛(ài)平,朱 彬,銀 燕,等.黃山頂夏季氣溶膠數(shù)濃度特征及其輸送潛在源區(qū)[J]. 中國(guó)環(huán)境科學(xué), 2014,34(4):852-861. Wang A P, Zhu B, Yin Y. Aerosol number concentration properties and potential sources areas transporting to the top of mountain Huangshan in summer [J]. China Environmental Science, 2014,34(4):852-861.

[27] 楊素英,張鐵凝,李艷偉,等.華東高海拔地區(qū)夏季氣溶膠數(shù)濃度及譜分布特征[J]. 大氣科學(xué)學(xué)報(bào), 2017,40(3):379-389. Yang S Y, Zhang T N, Li Y W, et al. Characteristics of aerosol number concentration and size distribution in summer in high altitude regions of East China [J]. Transactions of Atmospheric Sciences, 2017,40(3): 379-389.

[28] 邵玉海,丁 朔,南嘉良,等.三明市大氣顆粒物數(shù)濃度與粒徑分布季節(jié)特征[J]. 復(fù)旦學(xué)報(bào)(自然科學(xué)版), 2017,56(3):290-295. Shao Y H, Ding S, Nan J L, et al. Seasonal characteristics of number concentration and size distribution of atmospheric particles over Sanming [J]. Journal of Fudan University (Natural Science), 2017, 56(3):290-295.

[29] Wu Z J, Hu M, Lin L, et al. Particle number size distribution in the urban atmosphere of Beijing, China [J]. Atmospheric Environment, 2008,42(34):7967-7980.

[30] 宋 宇,唐孝炎,張遠(yuǎn)航,等.夏季持續(xù)高溫天氣對(duì)北京市大氣細(xì)粒子PM2.5的影響 [J]. 環(huán)境科學(xué), 2002,23(4):33-36. Song Y, Tang X Y, Zhang Y H, et al. Effects on fine particles by the continued high temperature weather in Beijing [J]. Environmental Science, 2002,23(4):33-36.

[31] 翟晴飛,金蓮姬,林振毅,等.石家莊春季大氣氣溶膠數(shù)濃度和譜的觀測(cè)特征[J]. 中國(guó)環(huán)境科學(xué), 2011,31(6):886-891. Zhai Q F, Jin L J, Lin Z Y, et al. Observational characteristic of aerosol number concentration and size distribution at Shijiazhuang in spring season [J]. China Environmental Science, 2011,31(6):886-891.

[32] 郎鳳玲,閆偉奇,張 泉,等.北京大氣顆粒物數(shù)濃度粒徑分布特征及與氣象條件的相關(guān)性[J]. 中國(guó)環(huán)境科學(xué), 2013,33(7):1153-1159. Lang F L, Yan W Q, Zhang Q, et al. Size distribution of atmospheric particle number in Beijing and association with meteorological conditions [J]. China Environmental Science, 2013,33(7):1153-1159.

[33] 謝小芳,孫 在,楊文俊.杭州市春季大氣超細(xì)顆粒物粒徑譜分布特征[J]. 環(huán)境科學(xué), 2014,35(2):436-441. Xie X F, Sun Z, Yang W J. Characterization of ultrafine particle size distribution in the urban atmosphere of Hangzhou in spring [J]. Environmental Science, 2014,35(2):436-441.

[34] Peng Y, Liu X, Dai J, et al. Aerosol size distribution and new particle formation events in the suburb of Xi'an, northwest China [J]. Atmospheric Environment, 2017,153:194-205.

[35] Du P, Gui H Q, Zhang J S, et al. Number size distribution of atmospheric particles in a suburban Beijing in the summer and winter of 2015 [J]. Atmospheric Environment, 2018,186:32-44.

[36] Shen X J, Sun J Y, Zhang X Y, et al. Particle climatology in Central East China retrieved from measurements in planetary boundary layer and in free troposphere at a 1500-m-High mountaintop site [J]. Aerosol and Air Quality Research, 2016,16(3):689–701.

[37] Zhang X, Yin Y, Lin Z, et al. Observation of aerosol number size distribution and new particle formation at a mountainous site in Southeast China [J]. Science of the Total Environment, 2017,575: 309-320.

[38] Kivekas N, Sun J, Zhan M, et al. Long term particle size distribution measurements at Mount Waliguan, a high-altitude site in inland China [J]. Atmospheric Chemistry and Physics, 2009,9:5461-5474.

[39] Gao J, Wang T, Zhou X H, et al. Measurement of aerosol number size distributions in the Yangtze River delta in China: Formation and growth of particles under polluted conditions [J]. Atmospheric Environment, 2009,43(4):829-836.

[40] Yue D L, Hu M, Wu Z J, et al. Characteristics of aerosol size distributions and new particle formation in the summer in Beijing [J]. Journal of Geophysical Research, 2009,114(14):1159-1171.

[41] Shen X J, Sun J Y, Zhang Y M, et al. First long-term study of particle number size distributions and new particle formation events of regional aerosol in the North China Plain [J]. Atmospheric Chemistry and Physics, 2011,11:1565-1580.

[42] Yue D L, Hua M, Wang Z B, et al. Comparison of particle number size distributions and new particle formation between the urban and rural sites in the PRD region, China [J]. Atmospheric Environment, 2013, 76(3): 181-188

[43] Gao J, Wang J, Cheng S H, et al. Number concentration and size distributions of submicron particles in Jinan urban area: Characteristics in summer and winter [J]. Environmental Science, 2007,19(12):1466-1473.

[44] 錢 凌,銀 燕,童堯青,等.南京北郊大氣細(xì)顆粒物的粒徑分布特征 [J]. 中國(guó)環(huán)境科學(xué), 2008,28(1):18-22. Qian L, Yin Y, Tong Y Q, et al. Characteristics of size distributions of atmospheric fine particles in the north suburban area of Nanjing [J]. China Environmental Science, 2008,28(1):18-22.

Number concentration of submicron aerosols at a rural site during summer harvest period.

ZHENG Shu-rui1, KONG Shao-fei1*, YAN Qin1, WU Jian1, ZHU Ge-hao1, YANG Guo-wei1, WU Fang-qi1, NIU Zhen-zhen1, ZHENG Ming-ming1,2, ZHENG Huang1, CHENG Yi1, CHENNan2, XU Ke2, QI Shi-hua1,3

(1.School of Environmental Studies, China University of Geosciences, Wuhan 430074, China；2.Hubei Environmental Monitoring Center, Wuhan 430074, China；3.State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China)., 2019,39(2)：478~486

A Scanning Mobility Particle Sizer was deployed at a typical rural site in North China Plain during June 2017 to study number concentration and size distribution of atmospheric submicron particles from the view of source regions. Results showed that the particles less than 300nm were dominant and the average particle number concentration was 28371cm-3. The distribution of particle number concentration in different modes showed obvious differences: Nucleation mode (<20nm) was linear distribution; Aitken mode (20~100nm) was multinomial distribution; Accumulation mode (>100nm) was logarithmic distribution. Result of 48h backward trajectories indicated that the total number concentration of particles increased by 66.7%, when the air masses were mainly affected by the biomass burning transmitting from the Jiangsu, Shandong and Anhui provinces. Potential source contribution function and concentration-weighted trajectory analysis demonstrated that potential source regions of particles were located in the east of the observation site, and the particles with particle diameter less than 100nm were dominant.

North China Plain；summer harvest period；rural site；aerosol number concentration；open biomass burning；source region

X513

A

1000-6923(2019)02-0478-09

鄭淑睿(1994-),女,湖北宜昌人,中國(guó)地質(zhì)大學(xué)(武漢)碩士研究生,主要從事民用燃料排放氣溶膠粒徑及混合狀態(tài)研究.發(fā)表論文1篇.

2018-07-14

科技部國(guó)家重點(diǎn)研發(fā)計(jì)劃課題(2016YFA0602002; 2017YFC0212602);湖北省科技廳技術(shù)創(chuàng)新專項(xiàng)重大項(xiàng)目(2017ACA089);中國(guó)地質(zhì)大學(xué)(武漢)高層次人才科研啟動(dòng)經(jīng)費(fèi)(201616);中央高?；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金資助項(xiàng)目-騰飛計(jì)劃(201802)

* 責(zé)任作者, 教授, kongshaofei@cug.edu.cn