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澳大利亚养猪场住房条件欠佳的风险因素识别:第1部分。研究理由与设计。

Identification of risk factors for sub-optimal housing conditions in Australian piggeries: Part 1. Study justification and design.

作者信息

Banhazi T M, Seedorf J, Rutley D L, Pitchford W S

机构信息

Livestock System Alliance, University of Adelaide, Roseworthy, Australia.

出版信息

J Agric Saf Health. 2008 Jan;14(1):5-20. doi: 10.13031/2013.24120.

Abstract

We undertook a literature search related to pig production facilities with two major aims: first, to review all the likely benefits that might be gained from air quality improvements; and second, to review previous research that had identified statistically significant factors affecting airborne pollutants and environmental parameters, so that these factors could be considered in a multifactorial analysis aimed at explaining variations in air pollutant concentrations. Ammonia, carbon dioxide, viable bacteria, endotoxins, and inhalable and respirable particles were identified as major airborne pollutants in the review. We found that high concentrations of airborne pollutants in livestock buildings could increase occupational health and safety risks, compromise the health, welfare, and production efficiency of animals, and affect the environment. Therefore, improving air quality could reduce environmental damage and improve animal and worker health. To achieve a reduction in pollutant concentrations, a better understanding of the factors influencing airborne pollutant concentrations in piggery buildings is required. Most of the work done previously has used simple correlation matrices to identify relationships between key factors and pollutant concentrations, without taking into consideration multifactorial effects simultaneously in a model. However, our review of this prior knowledge was the first important step toward developing a more inclusive statistical model. This review identified a number of candidate risk factors, which we then took into consideration during the development of multifactorial statistical models. We used a general linear model (GLM) to model measured internal concentrations, emissions, and environmental parameters in order to predict and potentially control the building environment.

摘要

我们进行了一项与养猪生产设施相关的文献检索,主要有两个目的:第一,回顾空气质量改善可能带来的所有潜在益处;第二,回顾之前已确定的影响空气传播污染物和环境参数的具有统计学意义的因素,以便在旨在解释空气污染物浓度变化的多因素分析中考虑这些因素。在此次综述中,氨、二氧化碳、活菌、内毒素以及可吸入和可呼吸颗粒物被确定为主要的空气传播污染物。我们发现,畜牧建筑中高浓度的空气传播污染物会增加职业健康和安全风险,损害动物的健康、福利和生产效率,并影响环境。因此,改善空气质量可以减少环境破坏,改善动物和工人的健康。为了降低污染物浓度,需要更好地了解影响养猪场建筑中空气传播污染物浓度的因素。此前的大多数工作都使用简单的相关矩阵来确定关键因素与污染物浓度之间的关系,而没有在模型中同时考虑多因素效应。然而,我们对这些先验知识的综述是朝着开发更具包容性的统计模型迈出的重要第一步。该综述确定了一些候选风险因素,我们随后在多因素统计模型的开发过程中对其进行了考虑。我们使用通用线性模型(GLM)对测量的内部浓度、排放和环境参数进行建模,以便预测并潜在地控制建筑环境。

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