Uncertainty analysis of air pollution under accidents of flue-gas treatment facilities for waste lead paste smelting regeneration
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摘要:
熔炼再生是重金属危险废物资源化利用最广泛的方式之一。熔炼设施废气处理单元失效条件下废气排放具有源强大、短期排放扩散参数偶然性强等特点，其环境后果呈现强随机性，对事故应急过程的精准监测和科学决策形成极大挑战。对此，提出高斯烟羽模型与随机响应面法耦合(GAUSS-SRSM)的风险评估方法，定量评估复杂源强、扩散参数及其不确定性条件下污染物的随机分布及概率特性。选择华北某再生铅企业开展案例研究，结果表明：在该区域典型气候条件下，下风向0.8~2.2和0.75~1.5 km处SO2和Pb浓度存在超标可能，最大落地浓度超标概率分别为44%和28%，以95%置信水平表征的暴露浓度分别为0.68、0.005 2 mg/m3，分别超过其GB 3095—2012《环境空气质量标准》二级标准限值1.36倍和1.16倍。季节性的风速和气温等差异导致同一设施不同季节的污染及其概率特征差异较大。以Pb为例，冬季较夏季存在超标可能的最大范围相差0.6 km，超标概率相差24%，暴露浓度相差0.003 9 mg/m3。气候和源强等的不确定性使得大气污染后果存在明显的不确定性，下风向0.5 km处不确定性最大，为3.85；随着距离变大，不确定性变小，3.0 km处仅为1.74。下风向0.8~2.2 km污染严重程度大、可能性高，需要避免在该区域布设污染敏感的设备或装置，且应作为事故后应急监测重点关注的区域；而0.5~1.2 km不确定性大，也需要通过加强监测频率等方式克服随机性误差。
Abstract:Smelting regenerating is one of the most widely used ways to recycle heavy metal hazardous waste. Under the failure of the flue-gas treatment units of smelting facilities, the exhaust gas emission has the characteristics of significant source intensity, strong randomness of short-term emission diffusion parameters, and strong randomness of the environmental consequences, which poses a great challenge to the accurate monitoring and scientific decision-making in the emergency response. In this regard, a risk assessment method based on Gaussian Plume Model-Stochastic Response Surface Method (GAUSS-SRSM) coupling was proposed to quantitatively evaluate the random distribution and probability characteristics of pollutants under complex source intensities, diffusion parameters and their uncertainties. An enterprise in North China was selected to carry out a case study. The results showed that under the typical climatic conditions of the region, the concentrations of SO2 and Pb at 0.8-2.2 km and 0.75-1.5 km downwind might exceed the standard. The probability of exceeding the maximum ground concentration limit was 44% and 28%, respectively. The exposure concentrations characterized at 95% confidence level were 0.68 and 0.005 2 mg/m3, which exceeded Ambient Air Quality Standards (GB 3095-2012) by 1.36 times and 1.16 times, respectively. Seasonal differences in wind speed and temperature, etc. led to large differences in pollution and its probability characteristics in different seasons at the same facility. In the case of Pb, for example, the maximum range of potential exceedances in winter differed by 0.6 km from that in summer, with 24% difference in exceedance probability and 0.003 9 mg/m3 difference in exposure concentration. The uncertainty of climate and source intensity made the air pollution consequences have obvious uncertainty, with the maximum uncertainty of 3.85 at 0.5 km downwind. As the distance increased, the uncertainty decreased, with only 1.74 at 3.0 km. As the large degree and high possibility of pollution occurred at 0.8-2.2 km downwind, it was necessary to avoid the deployment of pollution-sensitive equipment or devices in this area, and it should be used as a key area for emergency monitoring after the accident; while the uncertainty of 0.5-1.2 km was large, it was also necessary to overcome the random error by strengthening the monitoring frequency.
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图 1 随机响应面法-高斯烟羽耦合模型不确定性分析流程
Figure 1. Flow chart of uncertainty analysis for Gaussian Plume Model-Stochastic Response Surface Method coupling
图 4 不同置信水平最大落地点浓度占标率
Figure 4. Concentration ratio to standard at maximum landing sites in different confidence intervals
图 5 SO2和Pb暴露浓度超标倍数
Figure 5. Over standard multiples of SO2 and Pb exposure concentrations
图 6 SO2和Pb的浓度月变化
注：最外圈数字代表月份；污染物浓度单位为mg/m3。
Figure 6. Monthly changes of SO2 and Pb concentrations
表 1 参数取值范围
Table 1. Parameter value ranges
参数 物理意义 单位 输入值 概率分布 u 平均风速 m/s 2~5 均匀分布 Q1 SO2排放速率 mg/s 25 000~30 000 均匀分布 Q2 Pb排放速率 mg/s 300~470 均匀分布 Vs 烟气出口流速 m/s 5.7~8.7 均匀分布 Ts 烟流温度 K 373~413 均匀分布 Ta 环境大气温度 K 258.4~301.6 均匀分布 Pa 大气压力 hPa 950~990 均匀分布 Qv 烟气流量 m3/s 11.4~17.45 均匀分布 注：输入值根据该项目实测数据确定，其中SO2、Pb取未经处理时废气的排放速率；概率分布类型根据参考文献[28]，均服从均匀分布。 
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表 2 扩散系数取值范围
Table 2. Value range of diffusion coefficients
距离/km 水平扩散系数
（σy）/(m2/s)垂直扩散系数
（σz）/(m2/s)概率分布 0.5 31.4~39.2 17.3~19.6 均匀分布 0.8 50.1~56.7 25.0~29.0 均匀分布 1.0 61.4~69.3 30.1~34.1 均匀分布 1.2 73.5~79.6 35.2~39.2 均匀分布 1.5 89.9~96.6 42.8~50.1 均匀分布 1.8 106.5~111.7 51.0~54.3 均匀分布 2.0 118.0~124.0 55.2~58.3 均匀分布 3.0 170.0~176.0 76.1~82.1 均匀分布
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表 3 高斯烟羽模型不确定性输出统计指标
Table 3. Uncertainty output statistical indexes of Gaussian Plume Model
统计指标 下风向1.0 km 下风向2.0 km MC/
万次2-SRSM/
千次3-SRSM/
千次MC/
万次2-SRSM/
千次3-SRSM/
千次mean 0.577 5 0.603 5 0.589 5 0.493 6 0.523 0 0.503 5 SD 0.021 2 0.023 1 0.022 0 0.022 4 0.024 1 0.023 2 CV 0.542 8 0.599 0 0.572 3 0.627 9 0.687 5 0.653 0 kurt 3.160 1 2.742 7 3.356 6 3.611 9 3.162 9 3.821 8 skew 1.791 4 1.578 5 1.736 0 1.011 9 0.900 7 1.043 2 注：2-SRSM指2阶SRSM模拟，3-SRSM指3阶SRSM模拟。全文同。
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表 4 某区平均风速、气温的月变化
Table 4. Monthly changes of average wind speed and temperature in an area
月份 平均风速/（m/s） 平均气温/℃ 1 2.1 2.7 2 2.2 5.6 3 2.8 4.9 4 3.2 13.5 5 3.4 21.4 6 3.5 24.0 7 3.7 27.5 8 4.0 28.6 9 4.3 22.2 10 4.5 17.4 11 3.1 7.9 12 2.9 3.2
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