Data on air, river water and ground level concentration

Scientific data collected reveals the performance of the company in fulfilling the requirement set by the authority. The data can also display pollution trends, indicating the current quality of the environment.

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To measure air quality in Malaysia, the DOE use the Air Pollution Index (API) (Asian Development Bank, 2006). The API includes all major pollutants, namely ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2) and suspended particulate matter of less than 10 microns in size (PM10). Human health can be affected if these pollutants reach unsafe levels (DOE, 1997). CO is poisonous, colourless, odourless and tasteless (USEPA, 2016c). Prolonged exposure to CO can cause people to lose consciousness and suffocate because it displaces oxygen in the blood and deprives the heart, brain and other vital organs of oxygen (U.S. Department of Labour Occupational Safety and Health Administration, 2002).

NO2 is a reactive gas that can irritate the lungs and weaken resistance to respiratory infections (Niedell, 2004). According to the Ecological Society of America (2000), its presence in the air may lead to environmental problems such as acid rain and eutrophication67 in coastal waters. Exposure to SO2 may affect breathing, causing respiratory problems, alterations in pulmonary defences and aggravation of existing cardiovascular diseases (New Hampshire Department of Environmental Service, 2012). Concerns around PM10 exposure include breathing and respiratory problems, premature death, lung tissue damage and cancer (Nargesh, 2015). The DOE did not require Lynas to report on ozone because ozone readings were affected by many factors such as smoke produced by other factories. It is for this reason that the data have been disaggregated only for CO, NO2, SO2 and PM10.

LAMP commenced operations in 2012. However, data became available starting from 2011. Monitoring data were collected at four open spaces: A1, A2, A3 and A4.68 Table 4 presents the amounts of each pollutant detected in five separate years, alongside the averaging time used in collecting the data, limits set by the DOE (1978)69 _{and the WHO’s} (2005) recommended guidelines. Data were collected from the DOE, although the monitoring activities were performed and the data were provided by the Lynas consultant.70 _{Data were available in monthly instalments and have some missing values.} Daily basis data were also made available to the DOE by Lynas, but they were considered

67 _{Reduction of oxygen in water due to increase in nutrients that can destroy fish and marine life.} 68 _{A1 is located at the south east corner of LAMP’s site, A2 in the north east portion of the site, A3 at the}

south west corner of the site, and A4 in the west quadrant of the site.

69 _{This is an old guideline. Nevertheless, currently the DOE and all companies still use it. A new}

guideline from 2014 has been designed by the authority, but it is not well-established and in the reviewing process. Companies have been given a five-year time by the authority to comply with the new and more stringent rule.

70 _{The DOE used a self-guided regulation where the companies will do the monitoring activities by their}

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confidential and could not be accessed by anyone; thus, the numbers provided in Table 4.4 are generated through interpolation.

Table 4.4 Air quality components

Parameter Averaging Time Year A1 A2 A3 A4 DOE (1978) µg/m3 WHO (2005) µg/ m3 CO 8 hours 2011 <100 <100 <100 <100 10,000 - 2012 <100 <100 <100 <100 10,000 - 2013 <100 <100 <100 <100 10,000 - 2014 <100 <100 <100 <100 10,000 - 2015 7,540 8,440 9,030 9,690 10,000 - NO2 1 hour 2011 17.6 10.6 20.3 6 320 200 2012 3.89 1.8 4.26 4.63 320 200 2013 <5 <5 <5 <5 320 200 2014 <5 <5 <5 <5 320 200 2015 7.53 8.44 9.03 9.69 320 200 SO2 24 hours 2011 <5 <5 <5 <5 105 20 2012 <5 <5 <5 <5 105 20 2013 <5 <5 <5 <5 105 20 2014 <5 <5 <5 <5 105 20 2015 4.97 5.46 4.02 5.56 105 20 PM10 24 hours 2011 34.8 45.4 62.8 52.7 150 50 2012 32.1 26.64 45.64 36.11 150 50 2013 43.4 45.2 52.9 47.5 150 50 2014 43.2 39.17 36.38 49.7 150 50 2015 39.02 38.49 55.13 40.61 150 50

Source: Adapted from the DOE (2016) and author’s own calculations.

The result shows that LAMP pollution levels were below the requirements set by the DOE. Nevertheless, based on the WHO standards, LAMP has exceeded advisable limits of PM10 three times in five years, especially at site A3. CO and SO2 emissions, however, were quite static across all years, and were well below the set limits. Meanwhile, NO2 emissions could be seen to have fluctuated over the five years, but always below the DOE and WHO limits. This may be because LAMP uses a five-stage WGTP to filter its emissions, which shows that installing advanced technological equipment could reduce the quantum of pollution produced by the plant.

However, there are two concerns raised based on the results presented. First, the standards used by the DOE were established in 1978. In other words, they are old standards that

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needed to be revised to protect humans’ well-being. According to the WHO (2005) standard of PM10, the elderly, children and those who have respiratory problems such as asthma, emphysema and bronchitis are likely to be affected (USEPA, 2016c). Second, the CO emission was below the limit set in 2011 to 2014. Nevertheless, in 2015, the emission was close to the old standard set by the authority. Some respondents (1 per cent) did complain that they were suffering from shortness of breath and asthma, and acknowledged that their friends and officemates also faced similar problems. If these people are sensitive to PM10, a reduction in this pollutant must be made. However, whether these people became sick because of LAMP is unclear, as many factors contribute to health problems. Nevertheless, if LAMP keeps exceeding safe PM10 emission limits recommended by the WHO, it may create health problems for the locals, particularly its workers.

4.4.3.2 Data on river water quality

In order to categorise classes of water and their uses, the National Water Quality Standards for Malaysia (NWSM) is referred and shown in Table 4.5.

Table 4.5 DOE Water Quality Index classification

Parameter Class I II III IV V AN <0.1 0.1 – 0.3 0.3 – 0.9 0.9 – 2.7 > 2.7 BOD <1 1 – 3 3 – 6 6 – 12 > 12 COD <10 10 – 25 25 – 50 50 – 100 > 100 DO >7 5 – 7 3 – 5 1 – 3 < 1 pH >7.0 6.0 – 7.0 5.0 – 6.0 < 5.0 > 5.0 TSS <2.5 25 – 50 50 – 150 150 – 300 > 300 WQI >92.7 76.5 – 92.7 51.9 – 76.5 51 – 76.5 < 31.0 Source: Zainudin (2010).

Classes I to III indicate water qualities that can sustain macro-aquatic life with varying degrees of sensitivity (Zainudin, 2010), from very sensitive aquatic species to tolerant species. Class IV can be used for irrigation and Class V has minimal usage. The Water Quality Index (WQI) is used to simplify the extensive data found in the NWQS. It measures water quality using a combination of physical measures (temperature, turbidity, total dissolved solids, total suspended solids, etc.), chemical measures (pH level, DO, BOD, COD, etc.), biological readings (groups of microorganisms) and radioactive parameters (Department of Irrigation and Drainage, 2009).

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The DOE uses six parameters—dissolved oxygen, biological oxygen demand, chemical oxygen demand, suspended solids, pH and ammoniacal nitrogen—to calculate the WQI. Dissolved oxygen (DO) is free oxygen (O2) in the water (Fondriest Environmental, 2013a) that is important for respiration of aquatic’ life. A high level of DO indicates better water quality (Center for Innovation in Engineering and Science Education, 2017). The USEPA (2006) set the DO minimum level at 5.0 mg/l and a drop below this level will put aquatic life under stress. Biochemical oxygen demand (BOD) is the amount of oxygen consumed by bacteria and microorganisms in water (USEPA, 2012b). Discharge of effluents with high BOD will accelerate bacterial growth that consumes DO, which can be lethal for fish and aquatic insects (Brown and Caldwell, 2001).

The function of BOD is similar to chemical oxygen demand (COD), as both measure organic compounds in water. However, COD coverage is wider as it predicts oxygen requirements during the decomposition of organic matter and the oxidation of inorganic chemicals (Amneera et al., 2013). Theoretically, if COD and/or BOD concentrations are high, water is considered polluted. Power of hydrogen (pH), on a scale of 0 to 14, reflects how acidic or basic a body of water is, with lower numbers being more acidic and higher numbers more basic (Fondriest Environmental, 2013b). The optimum pH levels for fish are between 6.5 and 9, while levels between 0 and 3 and between 11 and 14 are fatal to organisms. Suspended solids (SS) are made up of organic and/or inorganic materials. Higher degrees of SS reduce water clarity and pollute water, and increase water temperatures because they absorb heat, hence reducing DO (Fondriest Environmental, 2013c).

Finally, ammoniacal nitrogen (AN) indicates nutrient status, organic enrichment and the health of a water body (Rožić et al., 2000). A higher value indicates water pollution, and the threshold level should be less than 5mg of AN per litre (Amneera et al., 2013). High concentrations of AN cause algae problems, which increases the demand for oxygen (USEPA, 2016b). In order to determine the WQI, the formula applied is as follows, where SIX = sub-index of X (Table 4.6):

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Table 4.6 Calculation of Water Quality Index (WQI)

Sub-index for DO (in saturation)

SIDO = 0 for DO < 8 (2a)

=100 for DO > 92 (2b)

= -0.395+0.030DO²-0.0002DO³ for 8 < DO < 92 (2c) Sub-index for BOD

SIBOD =100.4-4.23BOD for BOD < 5 (3a)

=108e-0.055BOD for BOD > 5 (3b)

Sub-index for COD

SICOD =-1.33COD+99.1 for COD < 20 (4a) =103e-0.0157COD-0.04COD for COD > 20 (4b) Sub-index for AN

SIAN =100.5-105AN for AN < 0.3 (5a) =94e-0.573AN-5 | AN-2| for 0.3 <AN < 4 (5b)

=0 for AN > 4 (5c)

Sub-index for SS

SISS =97.5e-0.00676SS+0.05SS for SS < 100 (6a) =71e-0.0016SS-0.015SS for 100 < SS < 1,000 (6b)

=0 for SS > 1,000 (6c)

Sub-index for pH

SIpH =17.2-17.2pH+5.02pH² for pH < 5.5 (7a) =-242+95.5pH-6.67pH² for 5.5 < pH < 7 (7b) =-181+82.4pH-6.05pH² for 7 <pH <8.75 (7c) =536-77.0pH+2.76pH² for pH > 8.75 (7d) Source: Zainudin (2010).

For this chapter, water sampling data collected by Lynas Corporation in the Balok river are drawn, paying particular attention to two of the eleven locations in which they were sampled: W10 (100m downstream from LAMP final discharge point) and W11 (750m upstream from LAMP final discharge point).

Based on graphing of COD, generated from interpolation technique, shown in Graph 4.1, in general, readings at W10 are much higher than those at W11 during the high tide (HT) and low tide (LT). From mid-2011 until the end of 2015, COD at W10 was Class III or IV, even reaching Class V at one time. COD at W11, meanwhile, was mostly Class III.

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Graph 4.1 Chemical Oxygen Demand for W10 and W11

Source: Author’s own calculations.

For BOD concentration too, shown in Graph 4.2, scores at W10 were much higher than at W11, even though most of the time both W10 and W11 were Class II. The high amount of COD and low amount of BOD indicate that oxidation of inorganic chemicals in the water was high relative to organic compounds.

Graph 4.2 Biochemical Oxygen Demand for W10 and W11

Source: Author’s own calculations.

For AN, shown in Graph 4.3, the data were volatile for both, but the nutrient level at W11 was much higher than at W10 that might encourage the development of algae. W10’s AN was mostly under Class III and sometimes in Class IV, whereas W11 was mostly under Class III but did reach as high as Class V at times.

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Graph 4.3 Ammoniacal Nitrogen for W10 and W11

Source: Author’s own calculations.

For SS, in Graph 4.4, W10 had exceptionally high values in June 2012, which put it into Class V. However, most of the time, SS at W10 was Class II. SS at W11 was also classified under Class II, but was sometimes in Class IV.

Graph 4.4 Suspended Solids for W10 and W11

Source: Author’s own calculations.

The values of COD, AN and SS being high, pollutants were likely reducing the amount of DO in the water. For the past five years at both sites, the value of DO was mostly below five, as can be seen in Graph 4.5, which places it below Class III. This means that sensitive aquatic life will be affected as it is below the minimum limit set by the USEPA, 5.0 mg/l.

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Graph 4.5 Dissolved Oxygen for W10 and W11

Source: Author’s own calculations.

As shown by Graph 4.6, pH values, meanwhile, have fluctuated at both sites, sometimes falling into Class II and sometimes into Class III.

Graph 4.6 pH for W10 and W11

Source: Author’s own calculations.

Based on the WQI, the overall river water quality across the five years at both sites can be categorised as either slightly polluted or polluted based on the DOE classification shown in Table 4.7.

Table 4.7 DOE Water Quality classification based on WQI

Parameters Index range

Clean Slightly polluted Polluted

SIBOD 91-100 80-90 0-79

SIAN 92-100 71-91 0-70

SISS 76-100 70-75 0-69

WQI 81-100 60-80 0-59

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The readings at W10 were more polluted compared to those at W11, as shown in Table 4.8, likely due to its proximity to LAMP (100m, compared to 750m for W11). However, this finding should be explained carefully, as pollution in both locations was technically ‘non-point source pollution’, meaning many other factories contribute to the deterioration of the water quality as well. Even though there is no specific data that can show a causal impact of LAMP’s waste on polluting the river water, the results show that neither location was natural enough to sustain very sensitive aquatic species, meaning their population numbers will suffer. Both locations require treatment to restore the condition of the water or aquatic flora and fauna will die out.

Table 4.8 Indication of Water Quality Index

Year W10 WQI Indication W11 WQI

Indication High Tide Low Tide High Tide Low Tide

2011 43.97 46.58 Polluted 60.61 60.03 Slightly polluted 2012 38.15 40.13 Polluted 48.11 51.14 Polluted 2013 62.28 62.84 Slightly polluted 63.46 63.95 Slightly polluted 2014 59.47 60.39 Slightly polluted-

polluted

57.7 57.60 Polluted 2015 52.30 55.57 Polluted 89.24 66.16 Slightly

polluted-clean Source: Author’s own calculations.

The above data support certain claims made by the local residents and fishermen that some fish were no longer found in the river, and that it had become difficult to obtain fish in the river. The fish that they were talking about were sensitive species that were unable to survive in the polluted water as they need high DO for respiration. The growth rate of the fish also could be affected because polluted water delays the hatching and affects the survival of fish’ eggs. The high amount of COD, AN and SS have not only made the river water become unclear, but they also contribute to a reduction in DO, which subsequently affects aquatic life.

4.3.3.3 Data on ground level concentration

The Gaussian dispersion model was used to predict the maximum ground level concentration (GLC). The model makes several assumptions: 1) plume spread in horizontal and vertical directions occurs primarily by diffusion along the direction of the mean wind, 2) source of pollution emission rate is in a steady-state, 3) wind speed, wind direction and atmospheric stability class are constant, 4) there is a mass transfer of

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pollutant, primarily due to bulk air motion in the x-direction, 5) no pollutant chemical transformation occurs and 6) wind speeds are ≥1m/sec, and limited to predicting concentration >50m downwind. The Gaussian dispersion model can be understood by using Figure 4.1 and its equation.

Figure 4.1 Gaussian dispersion model

Source: Pilat (2009). 𝐶(𝑥, 𝑦, 𝑧) = Q 2πu𝜎_𝑦𝜎_𝑧𝑢̅𝑒𝑥𝑝 [− 1 2( 𝑦2 𝜎_𝑦2+ (𝑧 − 𝐻)2 𝜎_𝑧2 )] where:

Q = pollution emission rate (gram/sec) H = stack height + plume rise (meters) U = wind speed (m/sec)

𝜎_𝑦 = horizontal crosswind dispersion coefficient (meters)

𝜎𝑧= vertical dispersion coefficient (meters)

𝜎_𝑦 and 𝜎_𝑧 depend on the atmospheric stability and atmospheric stability is defined in terms surface wind speed, incoming solar radiation and cloud cover. In a simplified equation, GLC can be estimated as follows:

𝐺𝑟𝑜𝑢𝑛𝑑 𝑙𝑒𝑣𝑒𝑙 𝑐𝑒𝑛𝑡𝑒𝑟 𝑙𝑖𝑛𝑒 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 (𝑦 = 0) − 𝑝𝑙𝑢𝑚𝑒 ℎ𝑒𝑖𝑔ℎℎ 𝐻,

𝐶(𝑥, 0,0; 𝐻) = Q

πu𝜎_𝑦𝜎_𝑧∙ [𝑒𝑥𝑝 (− 𝐻2

2𝜎_𝑧2)]

A linear log-log plot of sigma Y versus X is given in Graph 4.7. Here, X is distance from the plume in terms of meters. The coefficient for C stability is used during the day, which

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indicates a clear day with sun 35-60o above the horizon. Meanwhile the coefficient for D stability is used for the night, which indicates a clear day with the sun at 15-35o. The value for sigma z is taken from the horizontal dispersion coefficient in Graph 4.7.

Graph 4.7 Horizontal and vertical dispersion coefficients

Source: Pilat (2009).

When the input data were plugged into the equation, the estimated output of plume centreline concentrations generated (shown in Table 4.9). However, Table 4.9 presents the results during the day (C stability) because people are exposed to the concentration during the day. At night, the concentration level is higher (more or less than double) than the day because of the low turbulence.

Specifically, the average wind speed (u) is about 3 m/s, pollution emission rate (Q) is approximately 17,066,000 ug/s [stack volume flow (14 m3/s) x emission (1,219,000 ug/m3)]. H is 65 m. The result reveals that the ground level concentration at 1000m (represents in X column) is only about 1/100, 000th of the amount emitted (see C/Q column), showing the impact of LAMP pollution on soil quality is very small. This amount becomes smaller as the distance from LAMP increases, and respondents live up to 40,000m from the plume. The predicted concentration values are up to 40,000m, but because the values are very small (.54–1.62), they are not shown in this table. To get the averaging factors, column C/Q multiply with 1 (for 1 hour, .7 for 8 hours, .4 for 24 hours, .0 for 1 month and .08 for 1 year), the stack volume flow m3/s and emission ug/m3. All values for the averaging time factors are given, but in the data analysis monthly values are used.

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Table 4.9 Predicted ground level concentrations

Predicted plume centreline concentrations (Averaging time factors)

σy X (m) σz C/Q 1h 8h 24h 1 month 1 year 103 1000 70 9.6E-06 164.01 114.81 65.60 49.20 13.12 187 2000 110 4.3E-06 74.08 51.85 29.63 22.22 5.93 265 3000 150 2.4E-06 41.47 29.03 16.59 12.44 3.32 340 4000 200 1.5E-06 25.27 17.69 10.11 7.58 2.02 412 5000 240 1.0E-06 17.65 12.35 7.06 5.29 1.41 483 6000 280 7.6E-07 13.05 9.13 5.22 3.91 1.04 552 7000 320 5.9E-07 10.05 7.04 4.02 3.02 0.80 619 8000 350 4.8E-07 8.22 5.75 3.29 2.47 0.66 686 9000 375 4.1E-07 6.94 4.86 2.78 2.08 0.56 751 10000 420 3.3E-07 5.68 3.97 2.27 1.70 0.45 815 11000 440 2.9E-07 4.99 3.50 2.00 1.50 0.40 879 12000 460 2.6E-07 4.44 3.10 1.77 1.33 0.35 942 13000 480 2.3E-07 3.97 2.78 1.59 1.19 0.32 1005 14000 500 2.1E-07 3.58 2.50 1.43 1.07 0.29 1066 15000 550 1.8E-07 3.07 2.15 1.23 0.92 0.25 1128 16000 600 1.6E-07 2.66 1.86 1.06 0.80 0.21 1188 17000 620 1.4E-07 2.45 1.71 0.98 0.73 0.20 1248 18000 640 1.3E-07 2.26 1.58 0.90 0.68 0.18 1308 19000 680 1.2E-07 2.03 1.42 0.81 0.61 0.16 1368 20000 700 1.1E-07 1.88 1.32 0.75 0.57 0.15 Source: Author’s own calculations.

In document Community Perceptions and their Impacts on Averting Behaviours and Housing Prices: A Study of a Rare Earth Processing Plant (Page 107-119)