Human and Environmental Impact Produced by E-Waste Releases at Guiyu Region (China)

Abstract

Over the last decades, the amount of electronic waste (e-waste) has increased rapidly in the world. It has become one of the emerging problems of the twenty-first century. About 50–80% of e-waste from industrialized countries is exported to recycling centers in developing countries such as China, India, Pakistan, and the Philippines because of the lower wages for labor and less strict environmental and safety regulations in these countries. China, however, due to its size and population not only receives enormous quantities of used devices from developed countries but also generates tremendous amounts of domestic e-waste due to its fast consumption rates of electrical and electronic (EE) products. Guiyu, a town in the Guangdong Province in the southeast of China, was identified as the largest e-waste site in the world and the second most polluted spot, due to informal recycling processes (acid extraction for metals, open burning of wires to get copper), which release chemicals to the environment, representing a threat to human health, both to “recyclers” and to nearby citizens, and the environment. Measured data on environmental concentrations and human health are scarce and scattered. Hence, environmental modeling was applied in order to generate an overview over the distribution of selected hazardous substances due to informal recycling in Guiyu. As all available models have a specific focus and various pros and cons, four models were chosen, which cover different geographical scales and address different environmental compartments and objectives in order to assess the potential risk of the selected chemicals to humans and the environment.

These models have been applied to different scenarios, mainly for two chemicals, decabromodiphenyl ether (DeBDE) and lead (Pb). Emissions of DeBDE and Pb that represent the input to the models are based on the SFA (Substance Flow Analysis) developed for Guiyu presented in the chapter “Tracking Global Flows of E-Waste Additives by Using Substance Flow Analysis, With a Case Study in China [51].” In this chapter the results of the four models are presented and compared among them. The impact of the selected chemicals for the environment and human health at Guiyu region has been assessed on different scales, i.e., on a global, regional, and local scales.

Annex

1.1 USEtox Data Input

1.2 Euses Input Parameters

1.2.1 Continental and Regional Parameters

In Table 9, the parameters of the continental area and regional area are shown.

Table 9 Continental parameters

1.3 Meteorological Data

1.3.1 Population Parameters

Population parameters were shown in Table 11. Guiyu was considered Coastal Region to obtain the Daily intake from Li et al. [23]. If no data of distribution was found, normal distribution with standard deviation equal to the 10% of the mean was assumed (Table 10).

Table 10 Meteorological data

Table 11 Population parameters

1.4 Emissions

In Table 12, the EUSES inputs regarding emissions parameters are shown.

Table 12 Emission parameters in EU TGD2003 Spreadsheet

1.5 QWASI Data Input/Creation

Climate data concerning the average rain rate were obtained from Hong et al. [40]. Table 13 shows the input parameters for the QWASI approach

Table 13 The parameter used as input data for the QWASI approach

1.6 FUN Data Input/Creation

Air temperature, precipitation, and wind speed data were considered for the Shantou Province, representing the climatic conditions of the year 2010 (Figs. 8, 9, and 10) and were obtained from: http://www.tutiempo.net/en/Climate/Shantou/05-2010/593160.htm. Finally the data were created for a 10-year period repeating for 10 times the 2010 data.

Fig. 8

Monthly average precipitation

Fig. 9

Monthly average wind speed

Fig. 10

Monthly average air temperature

The soil temperature was created based on air temperature and the data are presented in the Fig. 11.

Fig. 11

Monthly average soil temperature

Pb concentration in air and surface water (SW) data used as inputs are presented in the Figs. 12 and 13 and were created by applying the following sinusoidal functions:

Fig. 12

Pb concentration in air entering the target region

Fig. 13

Pb concentration in surface water at upstream zone

$$ C{\text{ai}}{{\text{r}}_{{{\rm{flow}}(t)}}} = \frac{{{{c}_{{\max }}} + {{C}_{{\min }}}}}{2} + \frac{{{{C}_{{\max }}} + {{C}_{{\min }}}}}{2} \times \sin \left( {2\pi \frac{t}{{365}} + \frac{\pi }{2}} \right) $$

$$ C{\text{s}}{{\text{w}}_{{{\rm{flow}}(t)}}} = \frac{{{{c}_{{\max }}} + {{C}_{{\min }}}}}{2} + \frac{{{{C}_{{\max }}} + {{C}_{{\min }}}}}{2} \times \sin \left( {2\pi \frac{t}{{365}} - \frac{\pi }{2}} \right) $$

The minimum and maximum values used were:

• Air: min Pb concentration (summer) – 7.4 × 10⁻³ mg m⁻³

• Air: max Pb concentration (winter) – 4.4 × 10⁻⁴ mg m⁻³

• SW: min Pb concentration (winter) – 6.1 × 10⁻⁵ mg m⁻³

• SW: max Pb concentration (summer) – 3 × 10⁻⁶ mg m⁻³

The total suspended particles value considered as input was 1.24 × 10⁻⁴ g m⁻³ whereas the global solar radiation was set at 158.95. Table 14 shows the input parameters used in the form of probability density function(PDF) and which allow the probabilistic analysis and sensitivity analysis in terms of simulation outcomes.

Table 14 The parameters used for the sensitivity and uncertainty analyses for the model outputs