The detail information for this material is given in 132-Development needs for integrated monitoring. It is available on http://www.intarese.org.

What are the challenges by integrating data from multiple sources?
Challenges on data issues in general
There are missing data, noisy data and inconsistent data:
•    Missing data
o    Data is not always available
o    Missing data may be due to
–    equipment malfunction
–    inconsistent with other recorded data and thus deleted
–    data not entered due to misunderstanding
–    certain data may not be considered important at the time of entry
–    not register history or changes of the data
•    Noisy data
o    Q: What is noise?
o    A: Random error in a measured variable
o    Incorrect attribute values may be due to
–    faulty data collection instruments
–    data entry problems
–    data transmission problems
–    technology limitation
–    inconsistency in naming convention
•    Inconsistent data
o    When you examine a data plot, you might find that some points appear to dramatically differ from the rest of the data (e.g. inappropriate values, Males being pregnant, or having a negative age). In some cases, it is reasonable to consider such point’s outliers, or data values that do not appear to be consistent with the rest of the data.
o    Inconsistent data may be due to
–    data sample problem
–    equipment malfunction
–    data entry problem

Challenges on data issues in environment and health fields
Before examining statistical methods for linking various types of data, it is necessary to investigate data sources that are available for tracking and linking hazards, exposure, and health effects (Mather et al., 2004). Fundamental factors that provide confidence in the results of data linkage are data quality, appropriate use of the data, and consideration of data limitations. The quality of hazard, exposure, and HOD (Health Outcome Data) are diverse, and the uses and limitations of data outside of its original purpose are not yet well defined (Table 1).
•    Environmental data (hazard-exposure data)
Hazard data tell us about pollutants that may be found in the environment, which can cause potential health problem. In INTARESE, hazard data from environmental monitoring is intended for exposure assessment, which can determine the amount, duration, and pattern of exposure to the pollutant.
•    Biomonitoring data (exposure-dose data)
Biomonitoring is the direct measurements of environmental chemicals, their metabolites or reaction products in people, usually in blood, urine, hair or milk. Exposure is defined as contact between an agent and a target. Dose is defined as the amount of agent that enters a target after crossing an exposure surface. If the exposure surface is an intake dose, the dose is an absorbed dose/intake dose; otherwise, it is an intake dose. In INTARESE, exposure and dose data are intended to estimate how much of the certain pollutant it would take to cause varying degree of health effects that could lead to illnesses.
•    Health surveillance data (health effect data)
In general, health data includes mortality and morbidity (incidence). In practice, it generally relied on a small number of measures, such as the number of monitoring region deaths, age-adjusted death rates for the monitoring region, and survival. In addition, health surveillance data also include health behavior and determinants of behavior (for example, knowledge, attitudes, and beliefs). In INTARESE, health effect data are intended to be linked to hazard-exposure-dose data in the view to assess the risk for the certain pollutant to cause health problem in the general population.
•    Other relevant data (covariates)
Other relevant data may include residence, proximity to known health effect-causing sources, socioeconomic status, age, race, and adherence to treatment regimens that may be related to incidence and hazard/exposure.

 
Challenges on data gathering
In general, multiple monitoring programs are implemented by multiple organizations. In practice, using an integrated approach across multiple organizations presents a number of challenges (Zeng, 1999): (i) obtaining data from other agencies is often difficult, and in many cases will be impossible; (ii) legal restrictions often prevent access to a particular data set; (iii) it is also difficult to obtain the cooperation of agency heads, who will often decide whether to participate in data sharing; (iv) data sharing often requires compatibility between different computer systems as well as the availability of information system personnel; (v) data integration also requires the concurrence of system administrators, directors of programs, and services consumers; and (vi) in addition, more cost and time, few data standards, and information overload are also barriers to data integration across multiple organizations.

Challenges on data analyzing
Without considering the challenges on obtaining data, there are still a number of technical challenges:
•    Increase in data volume
•    Increasing need for interdisciplinary use of data
•    Integration of data among systems is needed to answer questions that address diverse societal benefits
•    Current data from monitoring systems already face challenges, for instance, with regard to spatial information in Europe: (i) fragmentation of data sets and sources (e.g. data is stored in disparate systems); (ii) gaps in data availability; (iii) lack of harmonization between data sets (e.g. data is using inconsistent formats, such as word processing, flat text files, mail messages, scanned images, spatial data files, audio/voice files, video clips, spreadsheet files, databases, graphics and CAD files) at different geographical scales make it difficult to access and use available spatial data throughout Europe (Smolders et al., 2008); and (iv) issues around data quality and accuracy.

Development needs for integrated monitoring
Integrated environment and health monitoring system
Even environmental health surveillance systems have been developed in the United States, Europe and Quebec (Abelsohn et al., 2009), there is still a development needs on establishing integrated environmental health monitoring/surveillance systems at national level for following goals:
•    Improving data availability and utility;
•    Developing the strategy on integrated monitoring and methodology on integrating data from different monitoring systems; and
•    Promoting knowledge translation for practical and policy.
Based upon the integrated monitoring framework developed in the D51 and D64.1, we hereby propose four categories of monitoring/surveillance information: environmental monitoring, ecosurveillance, biomonitoring and health outcome surveillance. The importance of linking environmental health surveillance with policy and action has led to the addition of a fifth category of information: the assessment of policy interventions.
To illustrate this point further, we have chosen PAHs (polycyclic aromatic hydrocarbons) and PCBs (polychlorinated biphenyls) as “worked examples”. Two worked examples are presented in next chapter.

Methods for integrating data from environment and health monitoring programs
Although there are numerous hazard, exposure, and health effect data sources available, few data linkage methods were identified by the data stewards. Therefore, there is a development needs on further developing data integration methodologies and approach for linking data from environment and health monitoring programs (e.g. environmental monitoring, ecosurveillance, biomonitoring, human health surveillance, etc.), including those enabling linkages between data sources (chemical, physical, biological, etc.), and for selected horizontal issues, e.g. as identified in WP1.5.
In last chapter, we have summarized the existing data integration methods. They are:
•    Geographical information systems (GIS)
•    Multiple Lines and Levels of Evidence (MLLE)
•    Bayesian Belief Networks (BBN)
•    Advanced statistical models
In addition, techniques for assessing uncertainty and techniques for quality assurance and quality control needs to be further developed as well.