| To investigate the role of urban green space in controlling airborne particulate matters (PM), landscape ecological forest, park green space and residential green space in Beijing were taken as research objects. During July,2013 to November,2014, concentration variations of PM with three diameters (TSP, PM10 and PM2.5) about three kinds of green space as well as chemical composition of PM2.5 only about the park were synchronously measured inside and outside of green space. This paper has studied the influence of weather conditions on green space controlling PM concentrations and their time change regulation, established a prediction model of PM2.5 hourly average concentration inside of green space, compared the differences on chemical composition of PM2.5 between inside and outside of green space sites, and analyzed the correlation between PM removal ability of green space and plant distribution. The main conclusions are as follows:(1) Weather conditions could influence the PM removal percentage of green space, and the concentrations of PM had the same comparative law inside of green space and outside of green space during summer, autumn and spring. In sunny and dusty days, concentrations of TSP and PM10 were significantly lower inside of landscape ecological forest than those outside of forest; in cloudy and slight hazy days, concentrations of three sizes PM were all significantly lower inside of forest than those outside of forest; while the comparison result in foggy days was contrary. Daily mean concentrations of PM were lower in sunny days than those under other weather conditions; cloud, dust, haze and fog aggravated the PM pollution, fog droplets with haze nuclei could cause serious air pollution; increased amplitude of finer PM was larger in hazy days, while of coarser PM was larger in dry windy days.(2) The daily variation characteristics of PM inside of forest green space were affected by weather conditions and human activities. In sunny days, diurnal variation curves of PM levels showed "W" with two peaks in morning and evening peak hours. The concentrations of TSP and PM10 inside of forest were lower than those outside of forest during daytime and nighttime, while PM2.5 concentrations inside of forest were lower in 8:00～22:00, higher in 22:00～6:00 than those outside of forest. In sunny to foggy days, diurnal variation curves of PM levels showed "S" with higher concentrations being at night than in the daytime. Rising drastically time of PM concentrations inside of forest occurred earlier and dropping drastically time occurred later than those outside of forest. In dusty to hazy days, the curves of TSP and PM10 concentrations had big fluctuation, while the curve of PM2.5 concentrations had a little fluctuation, which continued to rise during nighttime. On the whole, the periods of a day in which atmosphere had better diffusion condition and lower PM levels were 11:00～16:00, thus this time was suitable for outdoor activities.(3) PM reduction effect of green space was closely related to their concentrations and the growth rhythm of plants. This reduction effect was higher in summer and spring, with the highest removal amount happening in spring. In this season, landscape ecological forest made the average concentrations of TSP, PM10and PM2.5 decreased by 65.44±8.58 μg·m-3,26.19±9.72 μg·m-3,12.16±4.5 μg·m-3, respectively; and park green space made the average concentration of TSP decreased by 129.12±30.81 μg·m-3. Generally speaking, the removal amount of PM reduced by green space was high during the periods with high PM levels and vigorous growth of plants.(4) There were different coefficient correlations between PM concentrations and different meteorological factors, in which air relative humidity, wind speed and wind direction were the main factors affecting particles concentrations. TSP and PM10 concentrations were less likely to be affected by meteorological factors than PM2.5 concentration, which had significant linear positive correlation with relative humidity and nonlinear negative correlation with wind speed. Southerly wind had a major role in exacerbating the air pollution, while northerly flow played the role of effective dilution of these matters in Beijing. Based on the connections between PM2.5 concentrations and pollution intensity and meteorological parameters, prediction models of PM2.5 hourly concentrations in green space were built using BP artificial neural network and multiple linear regression. This developed model using BP artificial neural network had a better prediction effect, and it works better for PM2.5 concentrations prediction in general pollution than during high pollution.(5) Plant configuration mode had different degree of influence on reduction rate of PM. The purification ability of lawn with few trees to PM was significantly lower than that of woodland; PM removal percentage of mixed broadleaf-conifer forest was larger than that of broad-leaved forest and coniferous forest; there was no obvious difference of PM removal percentage between arbor-shrub-law planting mode and arbor-law mode. There were insignificant positive correlations between the PM reduction rate of green space and plant height and crown width. The stand with too large density or crown density was not conducive to the growth of plants, hindered aeration and promoted bacterial growth, thus leading to a rise in PM concentrations in green space, this kind of negative effect was particularly apparent under hot and humid climate in summer and autumn. When the density was 30～45 per mu for trees with large crown, and 50～60 per mu for trees with narrow crown, the PM reduction effects of green space were obvious; when the density was 93 per mu for trees with narrow crown, the concentrations were higher in green space than those in control spot. In addition, plant configuration of green space had a greater impact on PM2.5 removal rate than that of coarse particulate.(6) The chemical compositions of PM2.5 were influenced obviously by park green space. Organic matter, SO42-, NO3-, NH4+, and crustal matter were the dominant species for PM2.5. The concentrations of OC, SOC, SO42-, NO3-, NH4+a nd K+ in PM2.5 inside of forest were markedly higher than those outside of forest in Olympic Forest Park; while the concentrations of PAHs and BEQ inside of forest were lower. The acidity of PM2.5 varied among different seasons. In winter and summer, particles were acidic; while, in spring and autumn, particles were alkaline. Besides, the particles inside of forest were more acidic than those outside of forest. Concentrations of crustal elements were the highest in spring, pollutant elements were the highest in winter. Comparing annual mean concentrations of some toxic elements in Olympic Forest Park with WHO standard, the mean concentration of As was 1.4 times of WHO standard, Cr was 30 times of the standard, the others were less than WHO standard; the equivalent toxicity of BaP was at a quite low level in Olympic Forest Park.(7) According to location and function, residential green space was divided into road green space, square green space, housing greenbelt, etc. For each type of green space, effects of PM reduction by different plant configuration modes were discussed. From the view of controlling air PM, also considering the landscape effect and the other ecological benefits (e.g. overshadow and noise reduction effects), some related suggestions were proposed. |
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