Measuring mining safety with injury statistics: lost workdays as indicators of risk

PROBLEM: Mining in the United States remains one of the most hazardous industries, despite significant reductions in fatal injury rates over the last century. Coal mine fatality rates, for example, have dropped almost a thousand-fold since their peak in 1908. While incidence rates are very important indicators, lost worktime measures offer an alternative metric for evaluating job safety and health performance. The first objective of this study examined the distributions and summary statistics of all injuries reported to the Mine Safety and Health Administration from 1983 through 2004. Over the period studied (1983-2004), there were 31,515,368 lost workdays associated with mining injuries, for an equivalent of 5,700 person-years lost annually. The second objective addressed the problem of comparing safety program performance in mines for situations where denominator data were lacking. By examining the consequences of injuries, comparisons can be made between disparate operations without the need for denominators. Total risk in the form of lost workday sums can help to distinguish between lower- and higher-risk operations or time periods. METHOD: Our method was to use a beta distribution to model the losses and to compare underground coal mining to underground metal/nonmetal mining from 2000 to 2004. RESULTS: Our results showed the probability of an injury having 10 or more lost workdays was 0.52 for coal mine cases versus 0.35 for metal/nonmetal mine cases. In addition, a comparison of injuries involving continuous mining machines over 2001-2002 versus 2003-2004 showed that the ratio of average losses in the later period to those in the earlier period was approximately 1.08, suggesting increasing risks for such operations. DISCUSSION: This denominator-free safety measure will help the mining industry more effectively identify higher-risk operations and more realistically evaluate their safety improvement programs. IMPACT ON INDUSTRY: Attention to a variety of metrics concerning the performance of a job safety and health program will enhance industry's ability to manage these programs and reduce risk.     

Role of secondary aerosols in haze formation in summer in the Megacity Beijing

A field experiment from 18 August to 8 September 2006 in Beijing, China, was carried out. A hazy day was defined as visibility < l0 km and RH (relative humidity) < 90%. Four haze episodes, which accounted for ~ 60% of the time during the whole campaign, were characterized by increases of SNA (sulfate, nitrate, and ammonium) and SOA (secondary organic aerosol) concentrations. The average values with standard deviation of SO₄^2 −, NO₃⁻, NH₄⁺ and SOA were 49.8 (± 31.6), 31.4 (± 22.3), 25.8 (± 16.6) and 8.9 (± 4.1) μg/m³, respectively, during the haze episodes, which were 4.3, 3.4, 4.1, and 1.7 times those in the non-haze days. The SO₄^2 −, NO₃⁻, NH₄⁺, and SOA accounted for 15.8%, 8.8%, 7.3%, and 6.0% of the total mass concentration of PM₁₀ during the non-haze days. The respective contributions of SNA species to PM₁₀ rose to about 27.2%, 15.9%, and 13.9% during the haze days, while the contributions of SOA maintained the same level with a slight decrease to about 4.9%. The observed mass concentrations of SNA and SOA increased with the increase of PM₁₀ mass concentration, however, the rate of increase of SNA was much faster than that of the SOA. The SOR (sulfur oxidation ratio) and NOR (nitrogen oxidation ratio) increased from non-haze days to hazy days, and increased with the increase of RH. High concentrations of aerosols and water vapor favored the conversion of SO₂ to SO₄^2 − and NO₂ to NO₃⁻, which accelerated the accumulation of the aerosols and resulted in the formation of haze in Beijing.     

Chemical characterization of size-resolved aerosols in four seasons and hazy days in the megacity Beijing of China

Size-resolved aerosol samples were collected by MOUDI in four seasons in 2007 in Beijing. The PM₁₀ and PM_1.8 mass concentrations were 166.0 ± 120.5 and 91.6 ± 69.7 μg/m³, respectively, throughout the measurement, with seasonal variation: nearly two times higher in autumn than in summer and spring. Serious fine particle pollution occurred in winter with the PM_1.8/PM₁₀ ratio of 0.63, which was higher than other seasons. The size distribution of PM showed obvious seasonal and diurnal variation, with a smaller fine mode peak in spring and in the daytime. OM (organic matter = 1.6 × OC (organic carbon)) and SIA (secondary inorganic aerosol) were major components of fine particles, while OM, SIA and Ca^2 + were major components in coarse particles. Moreover, secondary components, mainly SOA (secondary organic aerosol) and SIA, accounted for 46%–96% of each size bin in fine particles, which meant that secondary pollution existed all year. Sulfates and nitrates, primarily in the form of (NH₄)₂SO₄, NH₄NO₃, CaSO₄, Na₂SO₄ and K₂SO₄, calculated by the model ISORROPIA II, were major components of the solid phase in fine particles. The PM concentration and size distribution were similar in the four seasons on non-haze days, while large differences occurred on haze days, which indicated seasonal variation of PM concentration and size distribution were dominated by haze days. The SIA concentrations and fractions of nearly all size bins were higher on haze days than on non-haze days, which was attributed to heterogeneous aqueous reactions on haze days in the four seasons.     

2007~2016年北京天气分型与霾日的关联

采用COST733软件将北京地区2007~2016年的大气环流总体分为T1~T9种类型,研究其与霾日的关联性,并结合PM_2.5和臭氧地面观测,分析不同天气型对应的污染特征及气象参数分布规律.2007~2016年霾日发生概率21.5%,T4和T9型下霾日最多,T5和T8型最不利于霾日发生.9类天气型下霾日变化具有阶段性,2007~2012年（阶段一）霾日少且年际差异小,2013~2016年（阶段二）霾日增多.对9类天气型下霾日PM_2.5及臭氧变化进行分析,T1、T3、T4和T9型霾日多出现在秋冬季,PM_2.5日变化为逐时增加态势,4类天气型在第一阶段的白天有浓度波动增长形成的小峰值,但第二阶段减弱消失.大部分天气型的霾日,阶段二的PM_2.5浓度较阶段一降低,T7和T9型表现为增加,增幅分别为23.7%和3.9%.所有天气型霾日的臭氧日变化均为单峰型,峰值出现在下午,臭氧日均浓度最高为T8型.此外,阶段二与阶段一相比,T3、T5和T6型臭氧平均浓度增加,其中T5型增幅达到49.8%.将霾日与近地面气象要素关联分析,平均气温、风向、风速可以较好的解释臭氧浓度变化,而PM_2.5的变化特征不仅与气象要素相关,在一定程度上也体现了污染排放及区域联动减排的贡献,需两者结合才能更好探究PM_2.5浓度整体特征及细节变化.     

呼包鄂地区沙尘期间大气污染特征研究

为研究沙尘天气下典型大气污染特征,选择呼和浩特市、包头市、鄂尔多斯市（简称"呼包鄂地区"）为研究对象,分析了呼包鄂地区2016年春季3月1日-5月31日的PM₁₀与PM_2.5数据,利用CMB（化学质量平衡）受体模型、后向轨迹模型研究了呼包鄂地区污染源的特征,通过富集因子法评估了人类活动对沙尘天气的影响,最终通过特征比值法对沙尘天气与非沙尘天气进行了区分.结果表明:①沙尘天气与非沙尘天气时,呼包鄂地区PM₁₀、PM_2.5中主要化学组分均为Si、Ca、Al、Fe、OC、SO₄2-与NO₃-.沙尘天气时,ρ（PM₁₀）与ρ（PM_2.5）的差值大于非沙尘天气,较高的ρ（Al）、ρ（Si）、ρ（Ca）说明呼包鄂地区受到较大的沙尘天气影响.②CMB受体模型源解析结果表明,沙尘天气时扬尘源对PM₁₀与PM_2.5的贡献率分别为59.3%、48.7%,说明PM₁₀和PM_2.5的主要污染源均为扬尘源.后向轨迹模式模拟表明,呼包鄂地区的沙尘主要来自其西北部地区.③元素Na、K、Mg的EF（富集因子）在PM₁₀与PM_2.5中均大于1.0,Si、Cu的EF在PM_2.5中均大于1.0,但在PM₁₀中小于或等于1.0;依据EF表征级别,自然因素对PM_2.5和PM₁₀的影响程度均较大,并且PM_2.5受人类活动影响的程度大于PM₁₀.通过特征比值发现,ρ（Si）/ρ（Al）大于1.7且ρ（Si）/ρ（Fe）大于2.2可作为呼包鄂地区典型沙尘天气的界定.研究显示,呼包鄂地区春季受到较大程度的沙尘影响,这些沙尘主要来自其西北部地区,通过特征比值法可以对呼包鄂地区沙尘天气进行界定.      

近50 a西北干旱区冬季积雪日数变化特征

论文基于西北干旱区104个站点1961—2010年的日积雪深度、日平均气温、日降水量数据和NCEP/NCAR数据,利用K-means聚类分析、Mann-Kendall法等方法,对西北干旱区冬季积雪日数的时空变化特征及其变化原因进行分析。结果表明:1)冬季积雪日数大值主要集中在阿尔泰山和准噶尔盆地地区,西辽河流域、鄂尔多斯高原、天山、塔里木、阿拉善高原地区冬季积雪日数相对较少,内蒙古高原地区冬季积雪日数基本呈由高纬地区向低纬地区减少的趋势。2)近50 a,西北干旱区冬季积雪日数呈增加趋势,且各区域主要在1984年发生突变现象。内蒙古高原地区内站点冬季积雪日数变化较大,其他分区内站点的冬季积雪日数基本无变化。3)西北干旱区各分区周期变化主要集中在5、10、25 a左右,其中25 a的周期变化最为明显。4)冬季东亚大槽、南支槽的减弱与西风的增强使西北干旱区降水量增加是导致西北干旱区冬季积雪日数增加的主要原因。     

雾霾天气预测中支持向量机的应用分析

支持向量机在对非线性复杂问题进行处理的过程中,展现出来的优势特征非常突出,本文针对雾霾天气预测中支持向量机的应用做出了进一步探究,对支持向量机的概念、支持向量机的基本思想、建立雾霾预测模型、预测试验给出了详细的分析。     

2011年重庆地区灰霾污染特征初步研究

本文结合2011年重庆区域灰霾统计结果表明，灰霾污染状况已逐步由重度和严重灰霾向轻中度灰霾污染过渡，污染程度得到好转。灰霾／非灰霾对比发现颗粒物PM2.5污染浓度较高，超标率较大，占颗粒物污染较大比重，且对能见度影响显著。2011年BC／PM2.5平均比值为18％左右，且比值时间序列变化趋势与全年灰霾污染总体发生趋势相符。     

灰霾期间气溶胶的污染特征

从颗粒物的时空分布和浓度水平方面综述了灰霾期间气溶胶的污染特征,介绍了灰霾期间气溶胶中金属元素、水溶性离子、有机碳和元素碳的浓度特征,以及颗粒物与能见度的相关性研究进展。指出:灰霾天气多发生在冬季,且气溶胶中PM2.5占的比重大;气溶胶污染与地理环境、气候条件、经济发展水平等有密切关系;水溶性离子多集中在PM2.5中;能见度的下降与气溶胶特别是细颗粒物有很大关系。提出目前灰霾研究中主要存在3大问题:一是对灰霾期间气溶胶中含有的有机物类别及其对不同季节发生灰霾的贡献率仍需进一步研究;二是灰霾期间气溶胶中有机物的形成机理尚不明确;三是不同源排放的气溶胶对灰霾形成的贡献率有待探讨。建议系统地开展大气细颗粒物有害成分的鉴定、源排放颗粒物的物理化学特性、扩散过程中各种物质间的反应和转化等方面的研究,为大气污染防治法规的制定提供依据。